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the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source_zh_cn -BUILDDIR = build_zh_cn - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/federated/docs/_ext/overwriteautosummary_generate.txt b/docs/federated/docs/_ext/overwriteautosummary_generate.txt deleted file mode 100644 index 4b0a1b1dd2..0000000000 --- a/docs/federated/docs/_ext/overwriteautosummary_generate.txt +++ /dev/null @@ -1,707 +0,0 @@ -""" - sphinx.ext.autosummary.generate - ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - Usable as a library or script to generate automatic RST source files for - items referred to in autosummary:: directives. - - Each generated RST file contains a single auto*:: directive which - extracts the docstring of the referred item. - - Example Makefile rule:: - - generate: - sphinx-autogen -o source/generated source/*.rst - - :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import argparse -import importlib -import inspect -import locale -import os -import pkgutil -import pydoc -import re -import sys -import warnings -from gettext import NullTranslations -from os import path -from typing import Any, Dict, List, NamedTuple, Sequence, Set, Tuple, Type, Union - -from jinja2 import TemplateNotFound -from jinja2.sandbox import SandboxedEnvironment - -import sphinx.locale -from sphinx import __display_version__, package_dir -from sphinx.application import Sphinx -from sphinx.builders import Builder -from sphinx.config import Config -from sphinx.deprecation import RemovedInSphinx50Warning -from sphinx.ext.autodoc import Documenter -from sphinx.ext.autodoc.importer import import_module -from sphinx.ext.autosummary import (ImportExceptionGroup, get_documenter, import_by_name, - import_ivar_by_name) -from sphinx.locale import __ -from sphinx.pycode import ModuleAnalyzer, PycodeError -from sphinx.registry import SphinxComponentRegistry -from sphinx.util import logging, rst, split_full_qualified_name, get_full_modname -from sphinx.util.inspect import getall, safe_getattr -from sphinx.util.osutil import ensuredir -from sphinx.util.template import SphinxTemplateLoader - -logger = logging.getLogger(__name__) - - -class DummyApplication: - """Dummy Application class for sphinx-autogen command.""" - - def __init__(self, translator: NullTranslations) -> None: - self.config = Config() - self.registry = SphinxComponentRegistry() - self.messagelog: List[str] = [] - self.srcdir = "/" - self.translator = translator - self.verbosity = 0 - self._warncount = 0 - self.warningiserror = False - - self.config.add('autosummary_context', {}, True, None) - self.config.add('autosummary_filename_map', {}, True, None) - self.config.add('autosummary_ignore_module_all', True, 'env', bool) - self.config.add('docs_branch', '', True, None) - self.config.add('branch', '', True, None) - self.config.add('cst_module_name', '', True, None) - self.config.add('copy_repo', '', True, None) - self.config.add('giturl', '', True, None) - self.config.add('repo_whl', '', True, None) - self.config.init_values() - - def emit_firstresult(self, *args: Any) -> None: - pass - - -class AutosummaryEntry(NamedTuple): - name: str - path: str - template: str - recursive: bool - - -def setup_documenters(app: Any) -> None: - from sphinx.ext.autodoc import (AttributeDocumenter, ClassDocumenter, DataDocumenter, - DecoratorDocumenter, ExceptionDocumenter, - FunctionDocumenter, MethodDocumenter, ModuleDocumenter, - NewTypeAttributeDocumenter, NewTypeDataDocumenter, - PropertyDocumenter) - documenters: List[Type[Documenter]] = [ - ModuleDocumenter, ClassDocumenter, ExceptionDocumenter, DataDocumenter, - FunctionDocumenter, MethodDocumenter, NewTypeAttributeDocumenter, - NewTypeDataDocumenter, AttributeDocumenter, DecoratorDocumenter, PropertyDocumenter, - ] - for documenter in documenters: - app.registry.add_documenter(documenter.objtype, documenter) - - -def _simple_info(msg: str) -> None: - warnings.warn('_simple_info() is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - print(msg) - - -def _simple_warn(msg: str) -> None: - warnings.warn('_simple_warn() is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - print('WARNING: ' + msg, file=sys.stderr) - - -def _underline(title: str, line: str = '=') -> str: - if '\n' in title: - raise ValueError('Can only underline single lines') - return title + '\n' + line * len(title) - - -class AutosummaryRenderer: - """A helper class for rendering.""" - - def __init__(self, app: Union[Builder, Sphinx], template_dir: str = None) -> None: - if isinstance(app, Builder): - warnings.warn('The first argument for AutosummaryRenderer has been ' - 'changed to Sphinx object', - RemovedInSphinx50Warning, stacklevel=2) - if template_dir: - warnings.warn('template_dir argument for AutosummaryRenderer is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - - system_templates_path = [os.path.join(package_dir, 'ext', 'autosummary', 'templates')] - loader = SphinxTemplateLoader(app.srcdir, app.config.templates_path, - system_templates_path) - - self.env = SandboxedEnvironment(loader=loader) - self.env.filters['escape'] = rst.escape - self.env.filters['e'] = rst.escape - self.env.filters['underline'] = _underline - - if isinstance(app, (Sphinx, DummyApplication)): - if app.translator: - self.env.add_extension("jinja2.ext.i18n") - self.env.install_gettext_translations(app.translator) - elif isinstance(app, Builder): - if app.app.translator: - self.env.add_extension("jinja2.ext.i18n") - self.env.install_gettext_translations(app.app.translator) - - def exists(self, template_name: str) -> bool: - """Check if template file exists.""" - warnings.warn('AutosummaryRenderer.exists() is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - try: - self.env.get_template(template_name) - return True - except TemplateNotFound: - return False - - def render(self, template_name: str, context: Dict) -> str: - """Render a template file.""" - try: - template = self.env.get_template(template_name) - except TemplateNotFound: - try: - # objtype is given as template_name - template = self.env.get_template('autosummary/%s.rst' % template_name) - except TemplateNotFound: - # fallback to base.rst - template = self.env.get_template('autosummary/base.rst') - - return template.render(context) - - -# -- Generating output --------------------------------------------------------- - - -class ModuleScanner: - def __init__(self, app: Any, obj: Any) -> None: - self.app = app - self.object = obj - - def get_object_type(self, name: str, value: Any) -> str: - return get_documenter(self.app, value, self.object).objtype - - def is_skipped(self, name: str, value: Any, objtype: str) -> bool: - try: - return self.app.emit_firstresult('autodoc-skip-member', objtype, - name, value, False, {}) - except Exception as exc: - logger.warning(__('autosummary: failed to determine %r to be documented, ' - 'the following exception was raised:\n%s'), - name, exc, type='autosummary') - return False - - def scan(self, imported_members: bool) -> List[str]: - members = [] - for name in members_of(self.object, self.app.config): - try: - value = safe_getattr(self.object, name) - except AttributeError: - value = None - - objtype = self.get_object_type(name, value) - if self.is_skipped(name, value, objtype): - continue - - try: - if inspect.ismodule(value): - imported = True - elif safe_getattr(value, '__module__') != self.object.__name__: - imported = True - else: - imported = False - except AttributeError: - imported = False - - respect_module_all = not self.app.config.autosummary_ignore_module_all - if imported_members: - # list all members up - members.append(name) - elif imported is False: - # list not-imported members - members.append(name) - elif '__all__' in dir(self.object) and respect_module_all: - # list members that have __all__ set - members.append(name) - - return members - - -def members_of(obj: Any, conf: Config) -> Sequence[str]: - """Get the members of ``obj``, possibly ignoring the ``__all__`` module attribute - - Follows the ``conf.autosummary_ignore_module_all`` setting.""" - - if conf.autosummary_ignore_module_all: - return dir(obj) - else: - return getall(obj) or dir(obj) - - -def generate_autosummary_content(name: str, obj: Any, parent: Any, - template: AutosummaryRenderer, template_name: str, - imported_members: bool, app: Any, - recursive: bool, context: Dict, - modname: str = None, qualname: str = None) -> str: - doc = get_documenter(app, obj, parent) - - def skip_member(obj: Any, name: str, objtype: str) -> bool: - try: - return app.emit_firstresult('autodoc-skip-member', objtype, name, - obj, False, {}) - except Exception as exc: - logger.warning(__('autosummary: failed to determine %r to be documented, ' - 'the following exception was raised:\n%s'), - name, exc, type='autosummary') - return False - - def get_class_members(obj: Any) -> Dict[str, Any]: - members = sphinx.ext.autodoc.get_class_members(obj, [qualname], safe_getattr) - return {name: member.object for name, member in members.items()} - - def get_module_members(obj: Any) -> Dict[str, Any]: - members = {} - for name in members_of(obj, app.config): - try: - members[name] = safe_getattr(obj, name) - except AttributeError: - continue - return members - - def get_all_members(obj: Any) -> Dict[str, Any]: - if doc.objtype == "module": - return get_module_members(obj) - elif doc.objtype == "class": - return get_class_members(obj) - return {} - - def get_members(obj: Any, types: Set[str], include_public: List[str] = [], - imported: bool = True) -> Tuple[List[str], List[str]]: - items: List[str] = [] - public: List[str] = [] - - all_members = get_all_members(obj) - for name, value in all_members.items(): - documenter = get_documenter(app, value, obj) - if documenter.objtype in types: - # skip imported members if expected - if imported or getattr(value, '__module__', None) == obj.__name__: - skipped = skip_member(value, name, documenter.objtype) - if skipped is True: - pass - elif skipped is False: - # show the member forcedly - items.append(name) - public.append(name) - else: - items.append(name) - if name in include_public or not name.startswith('_'): - # considers member as public - public.append(name) - return public, items - - def get_module_attrs(members: Any) -> Tuple[List[str], List[str]]: - """Find module attributes with docstrings.""" - attrs, public = [], [] - try: - analyzer = ModuleAnalyzer.for_module(name) - attr_docs = analyzer.find_attr_docs() - for namespace, attr_name in attr_docs: - if namespace == '' and attr_name in members: - attrs.append(attr_name) - if not attr_name.startswith('_'): - public.append(attr_name) - except PycodeError: - pass # give up if ModuleAnalyzer fails to parse code - return public, attrs - - def get_modules(obj: Any) -> Tuple[List[str], List[str]]: - items: List[str] = [] - for _, modname, _ispkg in pkgutil.iter_modules(obj.__path__): - fullname = name + '.' + modname - try: - module = import_module(fullname) - if module and hasattr(module, '__sphinx_mock__'): - continue - except ImportError: - pass - - items.append(fullname) - public = [x for x in items if not x.split('.')[-1].startswith('_')] - return public, items - - ns: Dict[str, Any] = {} - ns.update(context) - - if doc.objtype == 'module': - scanner = ModuleScanner(app, obj) - ns['members'] = scanner.scan(imported_members) - ns['functions'], ns['all_functions'] = \ - get_members(obj, {'function'}, imported=imported_members) - ns['classes'], ns['all_classes'] = \ - get_members(obj, {'class'}, imported=imported_members) - ns['exceptions'], ns['all_exceptions'] = \ - get_members(obj, {'exception'}, imported=imported_members) - ns['attributes'], ns['all_attributes'] = \ - get_module_attrs(ns['members']) - ispackage = hasattr(obj, '__path__') - if ispackage and recursive: - ns['modules'], ns['all_modules'] = get_modules(obj) - elif doc.objtype == 'class': - ns['members'] = dir(obj) - ns['inherited_members'] = \ - set(dir(obj)) - set(obj.__dict__.keys()) - ns['methods'], ns['all_methods'] = \ - get_members(obj, {'method'}, ['__init__']) - ns['attributes'], ns['all_attributes'] = \ - get_members(obj, {'attribute', 'property'}) - - if modname is None or qualname is None: - modname, qualname = split_full_qualified_name(name) - - if doc.objtype in ('method', 'attribute', 'property'): - ns['class'] = qualname.rsplit(".", 1)[0] - - if doc.objtype in ('class',): - shortname = qualname - else: - shortname = qualname.rsplit(".", 1)[-1] - - ns['fullname'] = name - ns['module'] = modname - ns['objname'] = qualname - ns['name'] = shortname - - ns['objtype'] = doc.objtype - ns['underline'] = len(name) * '=' - - if template_name: - return template.render(template_name, ns) - else: - return template.render(doc.objtype, ns) - - -def generate_autosummary_docs(sources: List[str], output_dir: str = None, - suffix: str = '.rst', base_path: str = None, - builder: Builder = None, template_dir: str = None, - imported_members: bool = False, app: Any = None, - overwrite: bool = True, encoding: str = 'utf-8') -> None: - - if builder: - warnings.warn('builder argument for generate_autosummary_docs() is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - - if template_dir: - warnings.warn('template_dir argument for generate_autosummary_docs() is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - - showed_sources = list(sorted(sources)) - if len(showed_sources) > 20: - showed_sources = showed_sources[:10] + ['...'] + showed_sources[-10:] - logger.info(__('[autosummary] generating autosummary for: %s') % - ', '.join(showed_sources)) - - if output_dir: - logger.info(__('[autosummary] writing to %s') % output_dir) - - if base_path is not None: - sources = [os.path.join(base_path, filename) for filename in sources] - - template = AutosummaryRenderer(app) - - # read - items = find_autosummary_in_files(sources) - - # keep track of new files - new_files = [] - - if app: - filename_map = app.config.autosummary_filename_map - else: - filename_map = {} - - # write - for entry in sorted(set(items), key=str): - if entry.path is None: - # The corresponding autosummary:: directive did not have - # a :toctree: option - continue - - path = output_dir or os.path.abspath(entry.path) - ensuredir(path) - - try: - name, obj, parent, modname = import_by_name(entry.name, grouped_exception=True) - qualname = name.replace(modname + ".", "") - except ImportExceptionGroup as exc: - try: - # try to import as an instance attribute - name, obj, parent, modname = import_ivar_by_name(entry.name) - qualname = name.replace(modname + ".", "") - except ImportError as exc2: - if exc2.__cause__: - exceptions: List[BaseException] = exc.exceptions + [exc2.__cause__] - else: - exceptions = exc.exceptions + [exc2] - - errors = list(set("* %s: %s" % (type(e).__name__, e) for e in exceptions)) - logger.warning(__('[autosummary] failed to import %s.\nPossible hints:\n%s'), - entry.name, '\n'.join(errors)) - continue - - context: Dict[str, Any] = {} - if app: - context.update(app.config.autosummary_context) - - content = generate_autosummary_content(name, obj, parent, template, entry.template, - imported_members, app, entry.recursive, context, - modname, qualname) - try: - py_source_rel = get_full_modname(modname, qualname).replace('.', '/') + '.py' - except: - logger.warning(name) - py_source_rel = '' - - re_view = f"\n.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/{app.config.docs_branch}/" + \ - f"resource/_static/logo_source_en.svg\n :target: " + app.config.giturl + \ - f"{app.config.copy_repo}/blob/{app.config.branch}/" + app.config.repo_whl + \ - py_source_rel.split(app.config.cst_module_name)[-1] + '\n :alt: View Source On Gitee\n\n' - - if re_view not in content and py_source_rel: - content = re.sub('([=]{5,})\n', r'\1\n' + re_view, content, 1) - filename = os.path.join(path, filename_map.get(name, name) + suffix) - if os.path.isfile(filename): - with open(filename, encoding=encoding) as f: - old_content = f.read() - - if content == old_content: - continue - elif overwrite: # content has changed - with open(filename, 'w', encoding=encoding) as f: - f.write(content) - new_files.append(filename) - else: - with open(filename, 'w', encoding=encoding) as f: - f.write(content) - new_files.append(filename) - - # descend recursively to new files - if new_files: - generate_autosummary_docs(new_files, output_dir=output_dir, - suffix=suffix, base_path=base_path, - builder=builder, template_dir=template_dir, - imported_members=imported_members, app=app, - overwrite=overwrite) - - -# -- Finding documented entries in files --------------------------------------- - -def find_autosummary_in_files(filenames: List[str]) -> List[AutosummaryEntry]: - """Find out what items are documented in source/*.rst. - - See `find_autosummary_in_lines`. - """ - documented: List[AutosummaryEntry] = [] - for filename in filenames: - with open(filename, encoding='utf-8', errors='ignore') as f: - lines = f.read().splitlines() - documented.extend(find_autosummary_in_lines(lines, filename=filename)) - return documented - - -def find_autosummary_in_docstring(name: str, module: str = None, filename: str = None - ) -> List[AutosummaryEntry]: - """Find out what items are documented in the given object's docstring. - - See `find_autosummary_in_lines`. - """ - if module: - warnings.warn('module argument for find_autosummary_in_docstring() is deprecated.', - RemovedInSphinx50Warning, stacklevel=2) - - try: - real_name, obj, parent, modname = import_by_name(name, grouped_exception=True) - lines = pydoc.getdoc(obj).splitlines() - return find_autosummary_in_lines(lines, module=name, filename=filename) - except AttributeError: - pass - except ImportExceptionGroup as exc: - errors = list(set("* %s: %s" % (type(e).__name__, e) for e in exc.exceptions)) - print('Failed to import %s.\nPossible hints:\n%s' % (name, '\n'.join(errors))) - except SystemExit: - print("Failed to import '%s'; the module executes module level " - "statement and it might call sys.exit()." % name) - return [] - - -def find_autosummary_in_lines(lines: List[str], module: str = None, filename: str = None - ) -> List[AutosummaryEntry]: - """Find out what items appear in autosummary:: directives in the - given lines. - - Returns a list of (name, toctree, template) where *name* is a name - of an object and *toctree* the :toctree: path of the corresponding - autosummary directive (relative to the root of the file name), and - *template* the value of the :template: option. *toctree* and - *template* ``None`` if the directive does not have the - corresponding options set. - """ - autosummary_re = re.compile(r'^(\s*)\.\.\s+(ms[a-z]*)?autosummary::\s*') - automodule_re = re.compile( - r'^\s*\.\.\s+automodule::\s*([A-Za-z0-9_.]+)\s*$') - module_re = re.compile( - r'^\s*\.\.\s+(current)?module::\s*([a-zA-Z0-9_.]+)\s*$') - autosummary_item_re = re.compile(r'^\s+(~?[_a-zA-Z][a-zA-Z0-9_.]*)\s*.*?') - recursive_arg_re = re.compile(r'^\s+:recursive:\s*$') - toctree_arg_re = re.compile(r'^\s+:toctree:\s*(.*?)\s*$') - template_arg_re = re.compile(r'^\s+:template:\s*(.*?)\s*$') - - documented: List[AutosummaryEntry] = [] - - recursive = False - toctree: str = None - template = None - current_module = module - in_autosummary = False - base_indent = "" - - for line in lines: - if in_autosummary: - m = recursive_arg_re.match(line) - if m: - recursive = True - continue - - m = toctree_arg_re.match(line) - if m: - toctree = m.group(1) - if filename: - toctree = os.path.join(os.path.dirname(filename), - toctree) - continue - - m = template_arg_re.match(line) - if m: - template = m.group(1).strip() - continue - - if line.strip().startswith(':'): - continue # skip options - - m = autosummary_item_re.match(line) - if m: - name = m.group(1).strip() - if name.startswith('~'): - name = name[1:] - if current_module and \ - not name.startswith(current_module + '.'): - name = "%s.%s" % (current_module, name) - documented.append(AutosummaryEntry(name, toctree, template, recursive)) - continue - - if not line.strip() or line.startswith(base_indent + " "): - continue - - in_autosummary = False - - m = autosummary_re.match(line) - if m: - in_autosummary = True - base_indent = m.group(1) - recursive = False - toctree = None - template = None - continue - - m = automodule_re.search(line) - if m: - current_module = m.group(1).strip() - # recurse into the automodule docstring - documented.extend(find_autosummary_in_docstring( - current_module, filename=filename)) - continue - - m = module_re.match(line) - if m: - current_module = m.group(2) - continue - - return documented - - -def get_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser( - usage='%(prog)s [OPTIONS] ...', - epilog=__('For more information, visit .'), - description=__(""" -Generate ReStructuredText using autosummary directives. - -sphinx-autogen is a frontend to sphinx.ext.autosummary.generate. It generates -the reStructuredText files from the autosummary directives contained in the -given input files. - -The format of the autosummary directive is documented in the -``sphinx.ext.autosummary`` Python module and can be read using:: - - pydoc sphinx.ext.autosummary -""")) - - parser.add_argument('--version', action='version', dest='show_version', - version='%%(prog)s %s' % __display_version__) - - parser.add_argument('source_file', nargs='+', - help=__('source files to generate rST files for')) - - parser.add_argument('-o', '--output-dir', action='store', - dest='output_dir', - help=__('directory to place all output in')) - parser.add_argument('-s', '--suffix', action='store', dest='suffix', - default='rst', - help=__('default suffix for files (default: ' - '%(default)s)')) - parser.add_argument('-t', '--templates', action='store', dest='templates', - default=None, - help=__('custom template directory (default: ' - '%(default)s)')) - parser.add_argument('-i', '--imported-members', action='store_true', - dest='imported_members', default=False, - help=__('document imported members (default: ' - '%(default)s)')) - parser.add_argument('-a', '--respect-module-all', action='store_true', - dest='respect_module_all', default=False, - help=__('document exactly the members in module __all__ attribute. ' - '(default: %(default)s)')) - - return parser - - -def main(argv: List[str] = sys.argv[1:]) -> None: - sphinx.locale.setlocale(locale.LC_ALL, '') - sphinx.locale.init_console(os.path.join(package_dir, 'locale'), 'sphinx') - translator, _ = sphinx.locale.init([], None) - - app = DummyApplication(translator) - logging.setup(app, sys.stdout, sys.stderr) # type: ignore - setup_documenters(app) - args = get_parser().parse_args(argv) - - if args.templates: - app.config.templates_path.append(path.abspath(args.templates)) - app.config.autosummary_ignore_module_all = not args.respect_module_all # type: ignore - - generate_autosummary_docs(args.source_file, args.output_dir, - '.' + args.suffix, - imported_members=args.imported_members, - app=app) - - -if __name__ == '__main__': - main() diff --git a/docs/federated/docs/_ext/overwriteobjectiondirective.txt b/docs/federated/docs/_ext/overwriteobjectiondirective.txt deleted file mode 100644 index e7ffdfe09a..0000000000 --- a/docs/federated/docs/_ext/overwriteobjectiondirective.txt +++ /dev/null @@ -1,374 +0,0 @@ -""" - sphinx.directives - ~~~~~~~~~~~~~~~~~ - - Handlers for additional ReST directives. - - :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import re -import inspect -import importlib -from functools import reduce -from typing import TYPE_CHECKING, Any, Dict, Generic, List, Tuple, TypeVar, cast - -from docutils import nodes -from docutils.nodes import Node -from docutils.parsers.rst import directives, roles - -from sphinx import addnodes -from sphinx.addnodes import desc_signature -from sphinx.deprecation import RemovedInSphinx50Warning, deprecated_alias -from sphinx.util import docutils, logging -from sphinx.util.docfields import DocFieldTransformer, Field, TypedField -from sphinx.util.docutils import SphinxDirective -from sphinx.util.typing import OptionSpec - -if TYPE_CHECKING: - from sphinx.application import Sphinx - - -# RE to strip backslash escapes -nl_escape_re = re.compile(r'\\\n') -strip_backslash_re = re.compile(r'\\(.)') - -T = TypeVar('T') -logger = logging.getLogger(__name__) - -def optional_int(argument: str) -> int: - """ - Check for an integer argument or None value; raise ``ValueError`` if not. - """ - if argument is None: - return None - else: - value = int(argument) - if value < 0: - raise ValueError('negative value; must be positive or zero') - return value - -def get_api(fullname): - """ - 获取接口对象。 - - :param fullname: 接口名全称 - :return: 属性对象或None(如果不存在) - """ - main_module = fullname.split('.')[0] - main_import = importlib.import_module(main_module) - - try: - return reduce(getattr, fullname.split('.')[1:], main_import) - except AttributeError: - return None - -def get_example(name: str): - try: - api_doc = inspect.getdoc(get_api(name)) - example_str = re.findall(r'Examples:\n([\w\W]*?)(\n\n|$)', api_doc) - if not example_str: - return [] - example_str = re.sub(r'\n\s+', r'\n', example_str[0][0]) - example_str = example_str.strip() - example_list = example_str.split('\n') - return ["", "**样例：**", ""] + example_list + [""] - except: - return [] - -def get_platforms(name: str): - try: - api_doc = inspect.getdoc(get_api(name)) - example_str = re.findall(r'Supported Platforms:\n\s+(.*?)\n\n', api_doc) - if not example_str: - example_str_leak = re.findall(r'Supported Platforms:\n\s+(.*)', api_doc) - if example_str_leak: - example_str = example_str_leak[0].strip() - example_list = example_str.split('\n') - example_list = [' ' + example_list[0]] - return ["", "支持平台："] + example_list + [""] - return [] - example_str = example_str[0].strip() - example_list = example_str.split('\n') - example_list = [' ' + example_list[0]] - return ["", "支持平台："] + example_list + [""] - except: - return [] - -class ObjectDescription(SphinxDirective, Generic[T]): - """ - Directive to describe a class, function or similar object. Not used - directly, but subclassed (in domain-specific directives) to add custom - behavior. - """ - - has_content = True - required_arguments = 1 - optional_arguments = 0 - final_argument_whitespace = True - option_spec: OptionSpec = { - 'noindex': directives.flag, - } # type: Dict[str, DirectiveOption] - - # types of doc fields that this directive handles, see sphinx.util.docfields - doc_field_types: List[Field] = [] - domain: str = None - objtype: str = None - indexnode: addnodes.index = None - - # Warning: this might be removed in future version. Don't touch this from extensions. - _doc_field_type_map = {} # type: Dict[str, Tuple[Field, bool]] - - def get_field_type_map(self) -> Dict[str, Tuple[Field, bool]]: - if self._doc_field_type_map == {}: - self._doc_field_type_map = {} - for field in self.doc_field_types: - for name in field.names: - self._doc_field_type_map[name] = (field, False) - - if field.is_typed: - typed_field = cast(TypedField, field) - for name in typed_field.typenames: - self._doc_field_type_map[name] = (field, True) - - return self._doc_field_type_map - - def get_signatures(self) -> List[str]: - """ - Retrieve the signatures to document from the directive arguments. By - default, signatures are given as arguments, one per line. - - Backslash-escaping of newlines is supported. - """ - lines = nl_escape_re.sub('', self.arguments[0]).split('\n') - if self.config.strip_signature_backslash: - # remove backslashes to support (dummy) escapes; helps Vim highlighting - return [strip_backslash_re.sub(r'\1', line.strip()) for line in lines] - else: - return [line.strip() for line in lines] - - def handle_signature(self, sig: str, signode: desc_signature) -> Any: - """ - Parse the signature *sig* into individual nodes and append them to - *signode*. If ValueError is raised, parsing is aborted and the whole - *sig* is put into a single desc_name node. - - The return value should be a value that identifies the object. It is - passed to :meth:`add_target_and_index()` unchanged, and otherwise only - used to skip duplicates. - """ - raise ValueError - - def add_target_and_index(self, name: Any, sig: str, signode: desc_signature) -> None: - """ - Add cross-reference IDs and entries to self.indexnode, if applicable. - - *name* is whatever :meth:`handle_signature()` returned. - """ - return # do nothing by default - - def before_content(self) -> None: - """ - Called before parsing content. Used to set information about the current - directive context on the build environment. - """ - pass - - def transform_content(self, contentnode: addnodes.desc_content) -> None: - """ - Called after creating the content through nested parsing, - but before the ``object-description-transform`` event is emitted, - and before the info-fields are transformed. - Can be used to manipulate the content. - """ - pass - - def after_content(self) -> None: - """ - Called after parsing content. Used to reset information about the - current directive context on the build environment. - """ - pass - - def check_class_end(self, content): - for i in content: - if not i.startswith('.. include::') and i != "\n" and i != "": - return False - return True - - def extend_items(self, rst_file, start_num, num): - ls = [] - for i in range(1, num+1): - ls.append((rst_file, start_num+i)) - return ls - - def run(self) -> List[Node]: - """ - Main directive entry function, called by docutils upon encountering the - directive. - - This directive is meant to be quite easily subclassable, so it delegates - to several additional methods. What it does: - - * find out if called as a domain-specific directive, set self.domain - * create a `desc` node to fit all description inside - * parse standard options, currently `noindex` - * create an index node if needed as self.indexnode - * parse all given signatures (as returned by self.get_signatures()) - using self.handle_signature(), which should either return a name - or raise ValueError - * add index entries using self.add_target_and_index() - * parse the content and handle doc fields in it - """ - if ':' in self.name: - self.domain, self.objtype = self.name.split(':', 1) - else: - self.domain, self.objtype = '', self.name - self.indexnode = addnodes.index(entries=[]) - - node = addnodes.desc() - node.document = self.state.document - node['domain'] = self.domain - # 'desctype' is a backwards compatible attribute - node['objtype'] = node['desctype'] = self.objtype - node['noindex'] = noindex = ('noindex' in self.options) - if self.domain: - node['classes'].append(self.domain) - node['classes'].append(node['objtype']) - - self.names: List[T] = [] - signatures = self.get_signatures() - for sig in signatures: - # add a signature node for each signature in the current unit - # and add a reference target for it - signode = addnodes.desc_signature(sig, '') - self.set_source_info(signode) - node.append(signode) - try: - # name can also be a tuple, e.g. (classname, objname); - # this is strictly domain-specific (i.e. no assumptions may - # be made in this base class) - name = self.handle_signature(sig, signode) - except ValueError: - # signature parsing failed - signode.clear() - signode += addnodes.desc_name(sig, sig) - continue # we don't want an index entry here - if name not in self.names: - self.names.append(name) - if not noindex: - # only add target and index entry if this is the first - # description of the object with this name in this desc block - self.add_target_and_index(name, sig, signode) - - contentnode = addnodes.desc_content() - node.append(contentnode) - if self.names: - # needed for association of version{added,changed} directives - self.env.temp_data['object'] = self.names[0] - self.before_content() - try: - example = get_example(self.names[0][0]) - platforms = get_platforms(self.names[0][0]) - except Exception as e: - example = '' - platforms = '' - logger.warning(f'Error API names in {self.arguments[0]}.') - logger.warning(f'{e}') - extra = platforms + example - if extra: - if self.objtype == "method": - self.content.data.extend(extra) - else: - index_num = 0 - for num, i in enumerate(self.content.data): - if i.startswith('.. py:method::') or self.check_class_end(self.content.data[num:]): - index_num = num - break - if index_num: - count = len(self.content.data) - for i in extra: - self.content.data.insert(index_num-count, i) - else: - self.content.data.extend(extra) - try: - self.content.items.extend(self.extend_items(self.content.items[0][0], self.content.items[-1][1], len(extra))) - except Exception as e: - logger.warning(f'{e}') - self.state.nested_parse(self.content, self.content_offset, contentnode) - self.transform_content(contentnode) - self.env.app.emit('object-description-transform', - self.domain, self.objtype, contentnode) - DocFieldTransformer(self).transform_all(contentnode) - self.env.temp_data['object'] = None - self.after_content() - return [self.indexnode, node] - - -class DefaultRole(SphinxDirective): - """ - Set the default interpreted text role. Overridden from docutils. - """ - - optional_arguments = 1 - final_argument_whitespace = False - - def run(self) -> List[Node]: - if not self.arguments: - docutils.unregister_role('') - return [] - role_name = self.arguments[0] - role, messages = roles.role(role_name, self.state_machine.language, - self.lineno, self.state.reporter) - if role: - docutils.register_role('', role) - self.env.temp_data['default_role'] = role_name - else: - literal_block = nodes.literal_block(self.block_text, self.block_text) - reporter = self.state.reporter - error = reporter.error('Unknown interpreted text role "%s".' % role_name, - literal_block, line=self.lineno) - messages += [error] - - return cast(List[nodes.Node], messages) - - -class DefaultDomain(SphinxDirective): - """ - Directive to (re-)set the default domain for this source file. - """ - - has_content = False - required_arguments = 1 - optional_arguments = 0 - final_argument_whitespace = False - option_spec = {} # type: Dict - - def run(self) -> List[Node]: - domain_name = self.arguments[0].lower() - # if domain_name not in env.domains: - # # try searching by label - # for domain in env.domains.values(): - # if domain.label.lower() == domain_name: - # domain_name = domain.name - # break - self.env.temp_data['default_domain'] = self.env.domains.get(domain_name) - return [] - -def setup(app: "Sphinx") -> Dict[str, Any]: - app.add_config_value("strip_signature_backslash", False, 'env') - directives.register_directive('default-role', DefaultRole) - directives.register_directive('default-domain', DefaultDomain) - directives.register_directive('describe', ObjectDescription) - # new, more consistent, name - directives.register_directive('object', ObjectDescription) - - app.add_event('object-description-transform') - - return { - 'version': 'builtin', - 'parallel_read_safe': True, - 'parallel_write_safe': True, - } - diff --git a/docs/federated/docs/_ext/overwriteviewcode.txt b/docs/federated/docs/_ext/overwriteviewcode.txt deleted file mode 100644 index 172780ec56..0000000000 --- a/docs/federated/docs/_ext/overwriteviewcode.txt +++ /dev/null @@ -1,378 +0,0 @@ -""" - sphinx.ext.viewcode - ~~~~~~~~~~~~~~~~~~~ - - Add links to module code in Python object descriptions. - - :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import posixpath -import traceback -import warnings -from os import path -from typing import Any, Dict, Generator, Iterable, Optional, Set, Tuple, cast - -from docutils import nodes -from docutils.nodes import Element, Node - -import sphinx -from sphinx import addnodes -from sphinx.application import Sphinx -from sphinx.builders import Builder -from sphinx.builders.html import StandaloneHTMLBuilder -from sphinx.deprecation import RemovedInSphinx50Warning -from sphinx.environment import BuildEnvironment -from sphinx.locale import _, __ -from sphinx.pycode import ModuleAnalyzer -from sphinx.transforms.post_transforms import SphinxPostTransform -from sphinx.util import get_full_modname, logging, status_iterator -from sphinx.util.nodes import make_refnode - - -logger = logging.getLogger(__name__) - - -OUTPUT_DIRNAME = '_modules' - - -class viewcode_anchor(Element): - """Node for viewcode anchors. - - This node will be processed in the resolving phase. - For viewcode supported builders, they will be all converted to the anchors. - For not supported builders, they will be removed. - """ - - -def _get_full_modname(app: Sphinx, modname: str, attribute: str) -> Optional[str]: - try: - return get_full_modname(modname, attribute) - except AttributeError: - # sphinx.ext.viewcode can't follow class instance attribute - # then AttributeError logging output only verbose mode. - logger.verbose('Didn\'t find %s in %s', attribute, modname) - return None - except Exception as e: - # sphinx.ext.viewcode follow python domain directives. - # because of that, if there are no real modules exists that specified - # by py:function or other directives, viewcode emits a lot of warnings. - # It should be displayed only verbose mode. - logger.verbose(traceback.format_exc().rstrip()) - logger.verbose('viewcode can\'t import %s, failed with error "%s"', modname, e) - return None - - -def is_supported_builder(builder: Builder) -> bool: - if builder.format != 'html': - return False - elif builder.name == 'singlehtml': - return False - elif builder.name.startswith('epub') and not builder.config.viewcode_enable_epub: - return False - else: - return True - - -def doctree_read(app: Sphinx, doctree: Node) -> None: - env = app.builder.env - if not hasattr(env, '_viewcode_modules'): - env._viewcode_modules = {} # type: ignore - - def has_tag(modname: str, fullname: str, docname: str, refname: str) -> bool: - entry = env._viewcode_modules.get(modname, None) # type: ignore - if entry is False: - return False - - code_tags = app.emit_firstresult('viewcode-find-source', modname) - if code_tags is None: - try: - analyzer = ModuleAnalyzer.for_module(modname) - analyzer.find_tags() - except Exception: - env._viewcode_modules[modname] = False # type: ignore - return False - - code = analyzer.code - tags = analyzer.tags - else: - code, tags = code_tags - - if entry is None or entry[0] != code: - entry = code, tags, {}, refname - env._viewcode_modules[modname] = entry # type: ignore - _, tags, used, _ = entry - if fullname in tags: - used[fullname] = docname - return True - - return False - - for objnode in list(doctree.findall(addnodes.desc)): - if objnode.get('domain') != 'py': - continue - names: Set[str] = set() - for signode in objnode: - if not isinstance(signode, addnodes.desc_signature): - continue - modname = signode.get('module') - fullname = signode.get('fullname') - try: - if fullname and modname==None: - if fullname.split('.')[-1].lower() == fullname.split('.')[-1] and fullname.split('.')[-2].lower() != fullname.split('.')[-2]: - modname = '.'.join(fullname.split('.')[:-2]) - fullname = '.'.join(fullname.split('.')[-2:]) - else: - modname = '.'.join(fullname.split('.')[:-1]) - fullname = fullname.split('.')[-1] - fullname_new = fullname - except Exception: - logger.warning(f'error_modename:{modname}') - logger.warning(f'error_fullname:{fullname}') - refname = modname - if env.config.viewcode_follow_imported_members: - new_modname = app.emit_firstresult( - 'viewcode-follow-imported', modname, fullname, - ) - if not new_modname: - new_modname = _get_full_modname(app, modname, fullname) - modname = new_modname - # logger.warning(f'new_modename:{modname}') - if not modname: - continue - # fullname = signode.get('fullname') - # if fullname and modname==None: - fullname = fullname_new - if not has_tag(modname, fullname, env.docname, refname): - continue - if fullname in names: - # only one link per name, please - continue - names.add(fullname) - pagename = posixpath.join(OUTPUT_DIRNAME, modname.replace('.', '/')) - signode += viewcode_anchor(reftarget=pagename, refid=fullname, refdoc=env.docname) - - -def env_merge_info(app: Sphinx, env: BuildEnvironment, docnames: Iterable[str], - other: BuildEnvironment) -> None: - if not hasattr(other, '_viewcode_modules'): - return - # create a _viewcode_modules dict on the main environment - if not hasattr(env, '_viewcode_modules'): - env._viewcode_modules = {} # type: ignore - # now merge in the information from the subprocess - for modname, entry in other._viewcode_modules.items(): # type: ignore - if modname not in env._viewcode_modules: # type: ignore - env._viewcode_modules[modname] = entry # type: ignore - else: - if env._viewcode_modules[modname]: # type: ignore - used = env._viewcode_modules[modname][2] # type: ignore - for fullname, docname in entry[2].items(): - if fullname not in used: - used[fullname] = docname - - -def env_purge_doc(app: Sphinx, env: BuildEnvironment, docname: str) -> None: - modules = getattr(env, '_viewcode_modules', {}) - - for modname, entry in list(modules.items()): - if entry is False: - continue - - code, tags, used, refname = entry - for fullname in list(used): - if used[fullname] == docname: - used.pop(fullname) - - if len(used) == 0: - modules.pop(modname) - - -class ViewcodeAnchorTransform(SphinxPostTransform): - """Convert or remove viewcode_anchor nodes depends on builder.""" - default_priority = 100 - - def run(self, **kwargs: Any) -> None: - if is_supported_builder(self.app.builder): - self.convert_viewcode_anchors() - else: - self.remove_viewcode_anchors() - - def convert_viewcode_anchors(self) -> None: - for node in self.document.findall(viewcode_anchor): - anchor = nodes.inline('', _('[源代码]'), classes=['viewcode-link']) - refnode = make_refnode(self.app.builder, node['refdoc'], node['reftarget'], - node['refid'], anchor) - node.replace_self(refnode) - - def remove_viewcode_anchors(self) -> None: - for node in list(self.document.findall(viewcode_anchor)): - node.parent.remove(node) - - -def missing_reference(app: Sphinx, env: BuildEnvironment, node: Element, contnode: Node - ) -> Optional[Node]: - # resolve our "viewcode" reference nodes -- they need special treatment - if node['reftype'] == 'viewcode': - warnings.warn('viewcode extension is no longer use pending_xref node. ' - 'Please update your extension.', RemovedInSphinx50Warning) - return make_refnode(app.builder, node['refdoc'], node['reftarget'], - node['refid'], contnode) - - return None - - -def get_module_filename(app: Sphinx, modname: str) -> Optional[str]: - """Get module filename for *modname*.""" - source_info = app.emit_firstresult('viewcode-find-source', modname) - if source_info: - return None - else: - try: - filename, source = ModuleAnalyzer.get_module_source(modname) - return filename - except Exception: - return None - - -def should_generate_module_page(app: Sphinx, modname: str) -> bool: - """Check generation of module page is needed.""" - module_filename = get_module_filename(app, modname) - if module_filename is None: - # Always (re-)generate module page when module filename is not found. - return True - - builder = cast(StandaloneHTMLBuilder, app.builder) - basename = modname.replace('.', '/') + builder.out_suffix - page_filename = path.join(app.outdir, '_modules/', basename) - - try: - if path.getmtime(module_filename) <= path.getmtime(page_filename): - # generation is not needed if the HTML page is newer than module file. - return False - except IOError: - pass - - return True - - -def collect_pages(app: Sphinx) -> Generator[Tuple[str, Dict[str, Any], str], None, None]: - env = app.builder.env - if not hasattr(env, '_viewcode_modules'): - return - if not is_supported_builder(app.builder): - return - highlighter = app.builder.highlighter # type: ignore - urito = app.builder.get_relative_uri - - modnames = set(env._viewcode_modules) # type: ignore - - for modname, entry in status_iterator( - sorted(env._viewcode_modules.items()), # type: ignore - __('highlighting module code... '), "blue", - len(env._viewcode_modules), # type: ignore - app.verbosity, lambda x: x[0]): - if not entry: - continue - if not should_generate_module_page(app, modname): - continue - - code, tags, used, refname = entry - # construct a page name for the highlighted source - pagename = posixpath.join(OUTPUT_DIRNAME, modname.replace('.', '/')) - # highlight the source using the builder's highlighter - if env.config.highlight_language in ('python3', 'default', 'none'): - lexer = env.config.highlight_language - else: - lexer = 'python' - highlighted = highlighter.highlight_block(code, lexer, linenos=False) - # split the code into lines - lines = highlighted.splitlines() - # split off wrap markup from the first line of the actual code - before, after = lines[0].split('
')
-        lines[0:1] = [before + '
', after]
-        # nothing to do for the last line; it always starts with 
 anyway
-        # now that we have code lines (starting at index 1), insert anchors for
-        # the collected tags (HACK: this only works if the tag boundaries are
-        # properly nested!)
-        maxindex = len(lines) - 1
-        for name, docname in used.items():
-            type, start, end = tags[name]
-            backlink = urito(pagename, docname) + '#' + refname + '.' + name
-            lines[start] = (
-                '
%s' % (name, backlink, _('[文档]')) +
-                lines[start])
-            lines[min(end, maxindex)] += '
'
-        # try to find parents (for submodules)
-        parents = []
-        parent = modname
-        while '.' in parent:
-            parent = parent.rsplit('.', 1)[0]
-            if parent in modnames:
-                parents.append({
-                    'link': urito(pagename,
-                                  posixpath.join(OUTPUT_DIRNAME, parent.replace('.', '/'))),
-                    'title': parent})
-        parents.append({'link': urito(pagename, posixpath.join(OUTPUT_DIRNAME, 'index')),
-                        'title': _('Module code')})
-        parents.reverse()
-        # putting it all together
-        context = {
-            'parents': parents,
-            'title': modname,
-            'body': (_('
Source code for %s
') % modname +
-                     '\n'.join(lines)),
-        }
-        yield (pagename, context, 'page.html')
-
-    if not modnames:
-        return
-
-    html = ['\n']
-    # the stack logic is needed for using nested lists for submodules
-    stack = ['']
-    for modname in sorted(modnames):
-        if modname.startswith(stack[-1]):
-            stack.append(modname + '.')
-            html.append('
    ')
-        else:
-            stack.pop()
-            while not modname.startswith(stack[-1]):
-                stack.pop()
-                html.append('
')
-            stack.append(modname + '.')
-        html.append('
  • %s
    • \n' % (
-            urito(posixpath.join(OUTPUT_DIRNAME, 'index'),
-                  posixpath.join(OUTPUT_DIRNAME, modname.replace('.', '/'))),
-            modname))
-    html.append('' * (len(stack) - 1))
-    context = {
-        'title': _('Overview: module code'),
-        'body': (_('
    All modules for which code is available
    ') +
-                 ''.join(html)),
-    }
-
-    yield (posixpath.join(OUTPUT_DIRNAME, 'index'), context, 'page.html')
-
-
-def setup(app: Sphinx) -> Dict[str, Any]:
-    app.add_config_value('viewcode_import', None, False)
-    app.add_config_value('viewcode_enable_epub', False, False)
-    app.add_config_value('viewcode_follow_imported_members', True, False)
-    app.connect('doctree-read', doctree_read)
-    app.connect('env-merge-info', env_merge_info)
-    app.connect('env-purge-doc', env_purge_doc)
-    app.connect('html-collect-pages', collect_pages)
-    app.connect('missing-reference', missing_reference)
-    # app.add_config_value('viewcode_include_modules', [], 'env')
-    # app.add_config_value('viewcode_exclude_modules', [], 'env')
-    app.add_event('viewcode-find-source')
-    app.add_event('viewcode-follow-imported')
-    app.add_post_transform(ViewcodeAnchorTransform)
-    return {
-        'version': sphinx.__display_version__,
-        'env_version': 1,
-        'parallel_read_safe': True
-    }
diff --git a/docs/federated/docs/requirements.txt b/docs/federated/docs/requirements.txt
deleted file mode 100644
index a1b6a69f6d..0000000000
--- a/docs/federated/docs/requirements.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-sphinx == 4.4.0
-docutils == 0.17.1
-myst-parser == 0.18.1
-sphinx_rtd_theme == 1.0.0
-numpy
-IPython
-jieba
diff --git a/docs/federated/docs/source_en/Data_Join.rst b/docs/federated/docs/source_en/Data_Join.rst
deleted file mode 100644
index 0b9712300b..0000000000
--- a/docs/federated/docs/source_en/Data_Join.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-Data Join
-=====================
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/Data_Join.rst
-    :alt: View Source On Gitee
-
-.. toctree::
-   :maxdepth: 1
-
-   data_join/data_join
-   data_join/private_set_intersection
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/communication_compression.md b/docs/federated/docs/source_en/communication_compression.md
deleted file mode 100644
index c797eb4d9c..0000000000
--- a/docs/federated/docs/source_en/communication_compression.md
+++ /dev/null
@@ -1,139 +0,0 @@
-# Device-Cloud Federated Learning Communication Compression
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/communication_compression.md)
-
-During the horizontal device-side federated learning training process, the traffic volume affects the user experience of the device-side (user traffic, communication latency, number of FL-Client participants) and is limited by the cloud-side performance constraints (memory, bandwidth, CPU usage). To improve user experience and reduce performance bottlenecks, MindSpore federated learning framework provides traffic compression for upload and download in device-cloud federated scenarios.
-
-## Compression Method
-
-### Uploading Compression Method
-
-The upload compression method can be divided into three main parts: weight difference codec, sparse codec and quantization codec. The flowcharts on FL-Client and FL-Server are given below.
-
-![Upload compression client execution order](./images/upload_compression_client_en.png)
-
-Fig.1 Flowchart of the upload compression method on FL-Client
-
-![Upload compression server execution order](./images/upload_compress_server_en.png)
-
-Fig.2 Flowchart of the upload compression method on FL-Server
-
-### Weight Difference Codec
-
-The weight difference is the vector difference of the weight matrix before and after the device-side training. Compared with the original weights, the distribution of the weight difference is more in line with the Gaussian distribution and therefore more suitable to be compressed. FL-Client performs the encoding operation on the weight difference, while FL-Server performs the decoding operation. Note that in order to reduce the weight difference to weights before FL-Server aggregates the weights, FL-Client does not multiply the weights by the amount of data when uploading the weights. When FL-Server decodes, it needs to multiply the weights by the amount of data.
-
-![Weight difference encoding](./images/weight_diff_encode_en.png)
-
-Fig.3 Flow chart of weight difference encoding on FL-Client
-
-![Weight difference decoding](./images/weight_diff_decode_en.png)
-
-Fig.4 Flow chart of weight difference decoding on FL-Server
-
-### Sparse Codec
-
-The device-side and cloud-side follow the same random algorithm to generate a sparse mask matrix that has the same shape as the original weights that need to be uploaded. The mask matrix contains only two values, 0 or 1. Each FL-Client only uploads data with the same weight as the non-zero value position of the mask matrix to the FL-Server.
-
-Take the sparse method with a sparse rate of sparse_rate=0.08 as an example. The parameters that are required to be uploaded by FL-Client:
-
-| Parameters               | Length  |
-| -------------------- | ----- |
-| albert.pooler.weight | 97344 |
-| albert.pooler.bias   | 312   |
-| classifier.weight    | 1560  |
-| classifier.bias      | 5     |
-
-Concatenate all parameters as one-dimensional vectors:
-
-| Parameters  |     Length             |
-| ----------- | ---------------------- |
-| merged_data | 97344+312+1560+5=99221 |
-
-Generate a mask vector with the same length as the concatenated parameter. There are 7937 values of 1, i.e., 7937 = int(sparse_rate*concatenated parameter length) and the rest have a value of 0, i.e., mask_vector = (1,1,1,... ,0,0,0,...):
-
-| Parameters  |     Length   |
-| ----------- | --------- |
-| mask_vector | 99221 |
-
-Use a pseudo-random algorithm to randomize the mask_vector. The random seed is the current number of iteration. Take out the indexes in the mask_vector with value 1. Take out the value of merged_data[indexes], i.e. the compressed vector.
-
-| Parameters  |     Length   |
-| ----------- | --------- |
-| compressed_vector | 7937 |
-
-After sparse compression, the parameter that FL-Client needs to upload is the compressed_vector.
-
-After receiving the compressed_vector, FL-Server first constructs the mask vector mask_vector with the same pseudo-random algorithm and random seeds as FL-Client. Then it takes out the indexes with the value of 1 in the mask_vector. Generate the all-zero matrix with the same shape as the model. The values in compressed_vector are put into weight_vector[indexes] in turn. weight_vector is the sparsely decoded vector.
-
-### Quantization Codec
-
-The quantization compression method is approximating communication data fixed-point of floating-point type to a finite number of discrete values.
-
-Taking the 8-bit quantization as an example:
-
-Quantify the number of bits num_bits = 8
-
-The floating-point data before compression is
-
-data = [0.03356021, -0.01842778, -0.009684053, 0.025363436, -0.027571501, 0.0077043395, 0.016391572, -0.03598478,  -0.0009508357]
-
-Compute the max and min values:
-
-min_val = -0.03598478
-
-max_val = 0.03356021
-
-Calculate scaling factor:
-
-scale = (max_val - min_val ) / (2 ^ num_bits - 1) = 0.000272725450980392
-
-Convert the pre-compressed data to an integer between -128 and 127 with the conversion formula quant_data = round((data - min_val) / scale) - 2 ^ (num_bits - 1). And strongly convert the data type to int8:
-
-quant_data = [127, -64, -32, 97, -97, 32, 64, -128, 0]
-
-After the quantitative encoding, the parameters that FL-Client needs to upload are quant_data and the minimum and maximum values min_val and max_val.
-
-After receiving quant_data, min_val and max_val, FL-Server uses the inverse quantization formula (quant_data + 2 ^ (num_bits - 1)) * (max_val - min_val) / (2 ^ num_bits - 1) + min_val to reduce the weights.
-
-## Downloading Compression Method
-
-The download compression method is mainly a quantization codec operation, and the flow charts on FL-Server and FL-Client are given below.
-
-![Download compression server execution order](./images/download_compress_server_en.png)
-
-Fig.5 Flowchart of the download compression method on FL-Server
-
-![Download compression client execution order](./images/download_compress_client_en.png)
-
-Fig.6 Flowchart of the download compression method on FL-Client
-
-### Quantization Codec
-
-The quantization codec is the same as that in upload compression.
-
-## Code Implementation Preparation
-
-To use the upload and download compression methods, first successfully complete the training aggregation process for either device or cloud federated scenario, e.g. [Implementing a Sentiment Classification Application (Android)](https://www.mindspore.cn/federated/docs/en/master/sentiment_classification_application.html). The preparation work including datasets and network models and the simulation of the process to initiate multi-client participation in federated learning are described in detail in this document.
-
-## Algorithm Open Script
-
-The upload and download compression methods are currently only supported in the device-cloud federated learning scenario. The open method requires setting `upload_compress_type='DIFF_SPARSE_QUANT'` and `download_compress_type='QUANT'` in the corresponding yaml in the server startup script when starting the cloud-side service. The above two hyperparameters control the upload and download compression methods on and off, respectively.
-
-The relevant parameter configuration to start the algorithm is given in the cloud-side [full startup script](https://gitee.com/mindspore/federated/tree/master/tests/st/cross_device_cloud/). After determining the parameter configuration, the user needs to configure the corresponding parameters before executing the training, as follows:
-
-```yaml
-compression:
-  upload_compress_type: NO_COMPRESS
-  upload_sparse_rate: 0.4
-  download_compress_type: NO_COMPRESS
-```
-
-| Hyperparameter Names and Reference Values        | Hyperparameter Description                                                     |
-| ---------------------- | ------------------------------------------------------------ |
-| upload_compress_type   | Upload compression type, string type, including: "NO_COMPRESS", "DIFF_SPARSE_QUANT" |
-| upload_sparse_rate     | Sparse ratio, i.e., weight retention, float type, defined in the domain (0, 1]            |
-| download_compress_type | Download compression type, string type, including: "NO_COMPRESS", "QUANT"       |
-
-## ALBERT Results
-
-The total number of federated learning iterations is 100. The number of client local training epochs is 1. The number of clients is 20. The batchSize is set to 16. The learning rate is 1e-5. Both upload and download compression methods are turned on. The upload sparse ratio is 0.4. The final accuracy on the validation set is 72.5%, and 72.3% for the common federated scenario without compression.
diff --git a/docs/federated/docs/source_en/conf.py b/docs/federated/docs/source_en/conf.py
deleted file mode 100644
index 882562b4e2..0000000000
--- a/docs/federated/docs/source_en/conf.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import shutil
-import sys
-import IPython
-import re
-import sphinx.ext.autosummary.generate as g
-from sphinx.ext import autodoc as sphinx_autodoc
-
-import mindspore
-
-# -- Project information -----------------------------------------------------
-
-project = 'MindSpore Federated'
-copyright = 'MindSpore'
-author = 'MindSpore'
-
-# The full version, including alpha/beta/rc tags
-release = 'master'
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-myst_enable_extensions = ["dollarmath", "amsmath"]
-
-
-myst_heading_anchors = 5
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode',
-    'myst_parser',
-    'sphinx.ext.mathjax',
-    'IPython.sphinxext.ipython_console_highlighting'
-]
-
-source_suffix = {
-    '.rst': 'restructuredtext',
-    '.md': 'markdown',
-}
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-mathjax_path = 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/mathjax/MathJax-3.2.2/es5/tex-mml-chtml.js'
-
-mathjax_options = {
-    'async':'async'
-}
-
-smartquotes_action = 'De'
-
-exclude_patterns = []
-
-pygments_style = 'sphinx'
-
-autodoc_inherit_docstrings = False
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'sphinx_rtd_theme'
-
-import sphinx_rtd_theme
-layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
-layout_src = '../../../../resource/_static/layout.html'
-if os.path.exists(layout_target):
-    os.remove(layout_target)
-shutil.copy(layout_src, layout_target)
-
-html_search_language = 'en'
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
-    'python': ('https://docs.python.org/3', '../../../../resource/python_objects.inv'),
-}
-
-# overwriteautosummary_generate add view source for api and more autosummary class availably.
-with open('../_ext/overwriteautosummary_generate.txt', 'r', encoding="utf8") as f:
-    exec(f.read(), g.__dict__)
-
-# Modify default signatures for autodoc.
-autodoc_source_path = os.path.abspath(sphinx_autodoc.__file__)
-autodoc_source_re = re.compile(r'stringify_signature\(.*?\)')
-get_param_func_str = r"""\
-import re
-import inspect as inspect_
-
-def get_param_func(func):
-    try:
-        source_code = inspect_.getsource(func)
-        if func.__doc__:
-            source_code = source_code.replace(func.__doc__, '')
-        all_params_str = re.findall(r"def [\w_\d\-]+\(([\S\s]*?)(\):|\) ->.*?:)", source_code)
-        all_params = re.sub("(self|cls)(,|, )?", '', all_params_str[0][0].replace("\n", "").replace("'", "\""))
-        return all_params
-    except:
-        return ''
-
-def get_obj(obj):
-    if isinstance(obj, type):
-        return obj.__init__
-
-    return obj
-"""
-
-with open(autodoc_source_path, "r+", encoding="utf8") as f:
-    code_str = f.read()
-    code_str = autodoc_source_re.sub('"(" + get_param_func(get_obj(self.object)) + ")"', code_str, count=0)
-    exec(get_param_func_str, sphinx_autodoc.__dict__)
-    exec(code_str, sphinx_autodoc.__dict__)
-
-import mindspore_federated
-
-# Copy source files of en python api from mindspore repository.
-src_dir_en = os.path.join(os.getenv("MF_PATH"), 'docs/api/api_python_en')
-present_path = os.path.dirname(__file__)
-
-for i in os.listdir(src_dir_en):
-    if os.path.isfile(os.path.join(src_dir_en,i)):
-        if os.path.exists('./'+i):
-            os.remove('./'+i)
-        shutil.copy(os.path.join(src_dir_en,i),'./'+i)
-    else:
-        if os.path.exists('./'+i):
-            shutil.rmtree('./'+i)
-        shutil.copytree(os.path.join(src_dir_en,i),'./'+i)
-
-# get params for add view source
-import json
-
-if os.path.exists('../../../../tools/generate_html/version.json'):
-    with open('../../../../tools/generate_html/version.json', 'r+', encoding='utf-8') as f:
-        version_inf = json.load(f)
-elif os.path.exists('../../../../tools/generate_html/daily_dev.json'):
-    with open('../../../../tools/generate_html/daily_dev.json', 'r+', encoding='utf-8') as f:
-        version_inf = json.load(f)
-elif os.path.exists('../../../../tools/generate_html/daily.json'):
-    with open('../../../../tools/generate_html/daily.json', 'r+', encoding='utf-8') as f:
-        version_inf = json.load(f)
-
-if os.getenv("MF_PATH").split('/')[-1]:
-    copy_repo = os.getenv("MF_PATH").split('/')[-1]
-else:
-    copy_repo = os.getenv("MF_PATH").split('/')[-2]
-
-branch = [version_inf[i]['branch'] for i in range(len(version_inf)) if version_inf[i]['name'] == copy_repo][0]
-docs_branch = [version_inf[i]['branch'] for i in range(len(version_inf)) if version_inf[i]['name'] == 'tutorials'][0]
-cst_module_name = 'mindspore_federated'
-repo_whl = 'mindspore_federated'
-giturl = 'https://gitee.com/mindspore/'
-
-sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
-# import anchor_mod
-import nbsphinx_mod
-
-sys.path.append(os.path.abspath('../../../../resource/search'))
-import search_code
-
-
-sys.path.append(os.path.abspath('../../../../resource/custom_directives'))
-from custom_directives import IncludeCodeDirective
-
-def setup(app):
-    app.add_directive('includecode', IncludeCodeDirective)
-    app.add_config_value('docs_branch', '', True)
-    app.add_config_value('branch', '', True)
-    app.add_config_value('cst_module_name', '', True)
-    app.add_config_value('copy_repo', '', True)
-    app.add_config_value('giturl', '', True)
-    app.add_config_value('repo_whl', '', True)
-
-src_release = os.path.join(os.getenv("MF_PATH"), 'RELEASE.md')
-des_release = "./RELEASE.md"
-with open(src_release, "r", encoding="utf-8") as f:
-    data = f.read()
-if len(re.findall("\n## (.*?)\n",data)) > 1:
-    content = re.findall("(## [\s\S\n]*?)\n## ", data)
-else:
-    content = re.findall("(## [\s\S\n]*)", data)
-#result = content[0].replace('# MindSpore', '#', 1)
-with open(des_release, "w", encoding="utf-8") as p:
-    p.write("# Release Notes"+"\n\n")
-    p.write(content[0])
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/cross_device.rst b/docs/federated/docs/source_en/cross_device.rst
deleted file mode 100644
index 9d20b10634..0000000000
--- a/docs/federated/docs/source_en/cross_device.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-Device-side Client
-======================
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/cross_device.rst
-    :alt: View Source On Gitee
-
-.. toctree::
-   :maxdepth: 1
-
-   java_api_callback
-   java_api_client
-   java_api_clientmanager
-   java_api_dataset
-   java_api_flparameter
-   java_api_syncfljob
-   interface_description_federated_client
diff --git a/docs/federated/docs/source_en/data_join.md b/docs/federated/docs/source_en/data_join.md
deleted file mode 100644
index c76aab8123..0000000000
--- a/docs/federated/docs/source_en/data_join.md
+++ /dev/null
@@ -1,241 +0,0 @@
-# Vertical Federated Learning Data Access
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/data_join.md)
-
-Unlike horizontal federated learning, two participants (leader and follower) have the same sample space for training or inference in vertical federated learning. Therefore, the data intersection must be done collaboratively before both parties in vertical federated learning initiate training or inference. Both parties must read their respective original data and extract the ID (unique identifier of each data, and none of them is the same) corresponding to each data for intersection (i.e., finding the intersection). Then, both parties obtain features or tags from the original data based on the intersected IDs. Finally, each side exports the persistence file and reads the data in the reordering manner before subsequent training or inference.
-
-## Overall Process
-
-Data access can be divided into two parts: data export and data read.
-
-### Exporting Data
-
-The MindSpore Federated vertical federated learning data export process framework is shown in Figure 1:
-
-![](./images/data_join_en.png)
-
-Fig. 1 Vertical Federated Learning Data Export Process Framework Diagram
-
-In the data export process, Leader Worker and Follower Worker are the two participants in the vertical federated learning. The Leader Worker is resident and keeps a listening ear on the Follower Worker, who can enter the data access process at any moment.
-
-After the Leader Worker receives a registration request from the Follower Worker, it checks the registration content. If the registration is successful, the task-related hyperparameters (PSI-related hyperparameters, bucketing rules, ID field names, etc.) are sent to the Follower Worker.
-
-The Leader Worker and Follower Worker read their respective raw data, extract the list of IDs from their raw data and implement bucketing.
-
-Each bucket of Leader Worker and Follower Worker initiates the privacy intersection method to obtain the ID intersections of the two parties.
-
-Finally, the two parties extract the corresponding data from the original data based on the ID intersections and export it to a file in MindRecord format.
-
-### Reading Data
-
-Vertical federated requires that both participants have the same value and order of data IDs for each batch of training or inference. MindSpore Federated ensures that the data is read in the same order by using the same random seed and by using dictionary sorting on the exported file sets when both parties read their respective data.
-
-## An Example for Quick Experience
-
-### Sample Data Preparation
-
-To use the data access method, the original data needs to be prepared first. The user can use [random data generation script](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/generate_random_data.py) to generate forged data for each participant as a sample.
-
-```shell
-python generate_random_data.py \
-    --seed=0 \
-    --total_output_path=vfl/input/total_data.csv \
-    --intersection_output_path=vfl/input/intersection_data.csv \
-    --leader_output_path=vfl/input/leader_data_*.csv \
-    --follower_output_path=vfl/input/follower_data_*.csv \
-    --leader_file_num=4 \
-    --follower_file_num=2 \
-    --leader_data_num=300 \
-    --follower_data_num=200 \
-    --overlap_num=100 \
-    --id_len=20 \
-    --feature_num=30
-```
-
-The user can set the hyperparameter according to the actual situation:
-
-| Hyperparameter names            | Hyperparameter description                                                     |
-| -------------------- | ------------------------------------------------------------ |
-| seed                 | Random seed, int type.                                          |
-| total_output_path    | The output path of all data, str type.                                |
-| intersection_output_path    | The output path of intersection data, str type.                                |
-| leader_output_path   | The export path of the leader data. If the configuration includes the `*`, the `*` will be replaced by the serial number of 0, 1, 2 ...... in order when exporting multiple files. str type. |
-| follower_output_path | The export path of the follower data. If the configuration includes the `*`, the `*` will be replaced by the serial number of 0, 1, 2 ...... in order when exporting multiple files. str type. |
-| leader_file_num      | The number of output files for leader data. int type.                        |
-| follower_file_num    | The number of output files for follower data. int type.                      |
-| leader_data_num      | The total number of leader data. int type.                                  |
-| follower_data_num    | The total number of follower data. int type.                                |
-| overlap_num          | The total amount of data that overlaps between leader and follower data. int type.                                |
-| id_len               | The data ID is a string type. The hyperparameter is the length of the string. int type.          |
-| feature_num          | The number of columns of the exported data                                           |
-
-Multiple csv files are generated after running the data preparation:
-
-```text
-follower_data_0.csv
-follower_data_1.csv
-intersection_data.csv
-leader_data_0.csv
-leader_data_1.csv
-leader_data_2.csv
-leader_data_3.csv
-```
-
-### Sample of Data Export
-
-Users can use [script of finding data intersections](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/run_data_join.py) to implement data intersections between two parties and export it to MindRecord format file. The users need to start Leader and Follower processes separately.
-
-Start Leader:
-
-```shell
-python run_data_join.py \
-    --role="leader" \
-    --main_table_files="vfl/input/leader/" \
-    --output_dir="vfl/output/leader/" \
-    --data_schema_path="vfl/leader_schema.yaml" \
-    --server_name=leader_node \
-    --http_server_address="127.0.0.1:1086" \
-    --remote_server_name=follower_node \
-    --remote_server_address="127.0.0.1:1087" \
-    --primary_key="oaid" \
-    --bucket_num=5 \
-    --store_type="csv" \
-    --shard_num=1 \
-    --join_type="psi" \
-    --thread_num=0
-```
-
-Start Follower:
-
-```shell
-python run_data_join.py \
-    --role="follower" \
-    --main_table_files="vfl/input/follower/" \
-    --output_dir="vfl/output/follower/" \
-    --data_schema_path="vfl/follower_schema.yaml" \
-    --server_name=follower_node \
-    --http_server_address="127.0.0.1:1087" \
-    --remote_server_name=leader_node \
-    --remote_server_address="127.0.0.1:1086" \
-    --store_type="csv" \
-    --thread_num=0
-```
-
-The user can set the hyperparameter according to the actual situation.
-
-| Hyperparameter names            | Hyperparameter description                                                |
-| ------------------- | ------------------------------------------------------- |
-| role                | Role types of the worker. str type. Including: "leader", "follower". |
-| main_table_files                  | The path of raw data, configure either single or multiple file paths, data directory paths, list or str types |
-| output_dir                        | The directory path of the exported MindRecord related files, str type.                |
-| data_schema_path         | The path of the super reference file to be configured during export, str type.         |
-| server_name      |Name of local http server that used for communication, str type.                             |
-| http_server_address      | Local IP and port address, str type.                             |
-| remote_server_name | Name of remote http server that used for communication, str type.                                |
-| remote_server_address | Peer IP and port address, str type.                                |
-| primary_key (Follower does not need to be configured) | The name of data ID, str type.                                      |
-| bucket_num (Follower does not need to be configured)  | Find the number of sub-buckets when intersecting and exporting, int type.                          |
-| store_type                        | Raw data storage type, str type. Including: "csv".                                  |
-| shard_num (Follower does not need to be configured)   | The number of files exported from a single bucket, int type.                              |
-| join_type (Follower does not need to be configured)   | Algorithm of intersection finding, str type. Including: "psi".                                          |
-| thread_num                        | Calculate the number of threads required when using the PSI intersection algorithm, int type.                 |
-
-In the above sample, the files corresponding data_schema_path can be referred to the corresponding files configuration of [leader_schema.yaml](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/vfl/leader_schema.yaml) and [follower_schema.yaml](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/vfl/follower_schema.yaml). The user needs to provide the column names and types of the data to be exported in this file.
-
-After running the data export, generate multiple MindRecord related files.
-
-```text
-mindrecord_0
-mindrecord_0.db
-mindrecord_1
-mindrecord_1.db
-mindrecord_2
-mindrecord_2.db
-mindrecord_3
-mindrecord_3.db
-mindrecord_4
-mindrecord_4.db
-```
-
-### Sample of Data Reading
-
-The user can use the [script of reading data](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/load_joined_data.py) to implement data reading after intersection.
-
-```shell
-python load_joined_data.py \
-    --seed=0 \
-    --input_dir=vfl/output/leader/ \
-    --shuffle=True
-```
-
-The user can set the hyperparameter according to the actual situation.
-
-| Hyperparameter names            | Hyperparameter description                                                |
-| --------- | ----------------------------------------- |
-| seed      | Random seed. int type.                       |
-| input_dir | The directory of the input MindRecord related files, str type. |
-| shuffle   | Whether the data order needs to be changed, bool type.              |
-
-If the intersection result is correct, when each of the two parties reads the data, the OAID order of each data of the two parties is the same, while the data of the other columns in each data can be different values. Print the intersection data after running the data read:
-
-```text
-Leader data export results:
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'uMbgxIMMwWhMGrVMVtM7')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'IwoGP08kWVtT4WHL2PLu')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'MSRe6mURtxgyEgWzDn0b')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'y7X0WcMKnTLrhxVcWfGF')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'DicKRIVvbOYSiv63TvcL')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'TCHgtynOhH3z11QYemsH')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'OWmhgIfC3k8UTteGUhni')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'NTV3qEYXBHqKBWyHGc7s')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'wuinSeN1bzYgXy4XmSlR')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'SSsCU0Pb46XGzUIa3Erg')}
-……
-
-Follower data export results:
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'uMbgxIMMwWhMGrVMVtM7')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'IwoGP08kWVtT4WHL2PLu')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'MSRe6mURtxgyEgWzDn0b')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'y7X0WcMKnTLrhxVcWfGF')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'DicKRIVvbOYSiv63TvcL')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'TCHgtynOhH3z11QYemsH')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'OWmhgIfC3k8UTteGUhni')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'NTV3qEYXBHqKBWyHGc7s')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'wuinSeN1bzYgXy4XmSlR')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'SSsCU0Pb46XGzUIa3Erg')}
-……
-```
-
-## An Example for Deep Experience
-
-For detailed API documentation for the following code, see [Data Access Documentation](https://www.mindspore.cn/federated/docs/en/master/data_join/data_join.html).
-
-### Data Export
-
-The user can implement data join and MindRecord related files export by using the encapsulated interface and yaml file in the following way:
-
-```python
-from mindspore_federated import FLDataWorker
-from mindspore_federated.common.config import get_config
-
-
-if __name__ == '__main__':
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    args = get_config(os.path.join(current_dir, "vfl/vfl_data_join_config.yaml"))
-    dict_cfg = args.__dict__
-
-    worker = FLDataWorker(config=dict_cfg)
-    worker.do_worker()
-```
-
-### Data Reading
-
-The user can implement data in exported MindRecord related files reading by using the encapsulated interface in the following way:
-
-```python
-from mindspore_federated.data_join import load_mindrecord
-
-
-if __name__ == "__main__":
-    dataset = load_mindrecord(input_dir="vfl/output/leader/", shuffle=True, seed=0)
-```
diff --git a/docs/federated/docs/source_en/deploy_federated_client.md b/docs/federated/docs/source_en/deploy_federated_client.md
deleted file mode 100644
index 479340ad31..0000000000
--- a/docs/federated/docs/source_en/deploy_federated_client.md
+++ /dev/null
@@ -1,202 +0,0 @@
-# Horizontal Federated Device-side Deployment
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/deploy_federated_client.md)
-
-This document describes how to compile and deploy Federated-Client.
-
-## Linux Compilation Guidance
-
-### System Environment and Third-party Dependencies
-
-This section describes how to complete the device-side compilation of MindSpore federated learning. Currently, the federated learning device-side only provides compilation guidance on Linux, and other systems are not supported. The following table lists the system environment and third-party dependencies required for compilation.
-
-| Software Name                 | Version  |  Functions |
-|-----------------------| ------------ | ------------ |
-| Ubuntu                | 18.04.02LTS   | Compiling and running MindSpore operating system  |
-| [GCC](#installing-gcc)         | Between 7.3.0 to 9.4.0  | C++ compiler for compiling MindSpore |
-| [git](#installing-git)         | -  | Source code management tools used by MindSpore |
-| [CMake](#installing-cmake)     | 3.18.3 and above  | Compiling and building MindSpore tools |
-| [Gradle](#installing-gradle)   | 6.6.1  | JVM-based building tools  |
-| [Maven](#installing-maven)     | 3.3.1 and above  | Tools for managing and building Java projects  |
-| [OpenJDK](#installing-openjdk) | Between 1.8 to 1.15  | Tools for managing and building Java projects  |
-
-#### Installing GCC
-
-Install GCC with the following command.
-
-```bash
-sudo apt-get install gcc-7 git -y
-```
-
-To install a higher version of GCC, use the following command to install GCC 8.
-
-```bash
-sudo apt-get install gcc-8 -y
-```
-
-Or install GCC 9.
-
-```bash
-sudo apt-get install software-properties-common -y
-sudo add-apt-repository ppa:ubuntu-toolchain-r/test
-sudo apt-get update
-sudo apt-get install gcc-9 -y
-```
-
-#### Installing git
-
-Install git with the following command.
-
-```bash
-sudo apt-get install git -y
-```
-
-#### Installing Cmake
-
-Install [CMake](https://cmake.org/) with the following command.
-
-```bash
-wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | sudo apt-key add -
-sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(lsb_release -cs) main"
-sudo apt-get install cmake -y
-```
-
-#### Installing Gradle
-
-Install [Gradle](https://gradle.org/releases/) with the following command.
-
-```bash
-# Download the corresponding zip package and unzip it.
-# Configure environment variables:
-  export GRADLE_HOME=GRADLE path
-  export GRADLE_USER_HOME=GRADLE path
-# Add the bin directory to the PATH:
-  export PATH=${GRADLE_HOME}/bin:$PATH
-```
-
-#### Installing Maven
-
-Install [Maven](https://archive.apache.org/dist/maven/maven-3/) with the following command.
-
-```bash
-# Download the corresponding zip package and unzip it.
-# Configure environment variables:
-  export MAVEN_HOME=MAVEN path
-# Add the bin directory to the PATH:
-  export PATH=${MAVEN_HOME}/bin:$PATH
-```
-
-#### Installing OpenJDK
-
-Install [OpenJDK](https://jdk.java.net/archive/) with the following command.
-
-```bash
-# Download the corresponding zip package and unzip it.
-# Configure environment variables:
-  export JAVA_HOME=JDK path
-# Add the bin directory to the PATH:
-  export PATH=${JAVA_HOME}/bin:$PATH
-```
-
-### Verifying Installation
-
-Verify that the installation in [System environment and third-party dependencies](#system-environment-and-third-party-dependencies) is successful.
-
-```text
-Open a command window and enter: gcc --version
-The following output identifies a successful installation:
-  gcc version version number
-
-Open a command window and enter: git --version
-The following output identifies a successful installation:
-  git version version number
-
-Open a command window and enter: cmake --version
-The following output identifies a successful installation:
-  cmake version version number
-
-Open a command window and enter: gradle --version
-The following output identifies a successful installation:
-  Gradle version number
-
-Open a command window and enter: mvn --version
-The following output identifies a successful installation:
-  Apache Maven version number
-
-Open a command window and enter: java --version
-The following output identifies a successful installation:
-  openjdk version version number
-
-```
-
-### Compilation Options
-
-The `cli_build.sh` script in the federated learning device_client directory is used for compilation on the federated learning device-side.
-
-#### Instructions for Using cli_build.sh Parameters
-
-| Parameters | Parameter Description                 | Value Range | Default Values       |
-| ---- | ------------------------ | -------- | ------------ |
-| -p   | the download path of dependency external packages | string   | third |
-| -c   | whether to reuse dependency packages previously downloaded | on and off  | on           |
-
-### Compilation Examples
-
-1. First, you need to download the source code from the gitee code repository before you can compile it.
-
-    ```bash
-    git clone https://gitee.com/mindspore/federated.git ./
-    ```
-
-2. Go to the mindspore_federated/device_client directory and execute the following command:
-
-    ```bash
-    bash cli_build.sh
-    ```
-
-3. Since the end-side framework and the model are decoupled, the x86 architecture package we provide, mindspore-lite-{version}-linux-x64.tar.gz, does not contain model-related scripts, so the user needs to generate the jar package corresponding to the model scripts. The jar package corresponding to the model scripts we provide can be obtained in the following way:
-
-    ```bash
-    cd federated/example/quick_start_flclient
-    bash build.sh -r mindspore-lite-java-flclient.jar # After -r, you need to give the absolute path to the latest x86 architecture package (generated in Step 2, federated/mindspore_federated/device_client/build/libs/jarX86/mindspore-lite-java-flclient.jar)
-    ```
-
-After running the above command, the path of generated jar package is federated/example/quick_start_flclient/target/quick_start_flclient.jar.
-
-### Building Dependency Environment
-
-1. After extracting the file `federated/mindspore_federated/device_client/third/mindspore-lite-{version}-linux-x64.tar.gz`, the obtained directory structure is as follows(files that are not used in federated  learning are not displayed here):
-
-    ```sh
-    mindspore-lite-{version}-linux-x64
-    ├── tools
-    └── runtime
-        ├── include  # Header files of training framework
-        ├── lib      # Training framework library
-        │   ├── libminddata-lite.a          # Static library files for image processing
-        │   ├── libminddata-lite.so        # Dynamic library files for image processing
-        │   ├── libmindspore-lite-jni.so   # jni dynamic library relied by MindSpore Lite inference framework
-        │   ├── libmindspore-lite-train.a  # Static library relied by MindSpore Lite training framework
-        │   ├── libmindspore-lite-train.so # Dynamic library relied by MindSpore Lite training framework
-        │   ├── libmindspore-lite-train-jni.so # jni dynamic library relied by MindSpore Lite training framework
-        │   ├── libmindspore-lite.a  # Static library relied by MindSpore Lite inference framework
-        │   ├── libmindspore-lite.so  # Dynamic library relied by MindSpore Lite inference framework
-        │   ├── mindspore-lite-java.jar    # MindSpore Lite training framework jar package
-        └── third_party
-            ├── glog
-            │└── libmindspore_glog.so.0   # Dynamic library files of glog
-            └── libjpeg-turbo
-                └── lib
-                    ├── libjpeg.so.62   # Dynamic library files for image processing
-                    └── libturbojpeg.so.0  # Dynamic library files for image processing
-    ```
-
-2. Put the so files relied by federated learning in paths `mindspore-lite-{version}-linux-x64/runtime/lib/`,  `mindspore-lite-{version}-linux-x64/runtime/third_party/glog/` and `mindspore-lite-{version}-linux-x64/runtime/third_party/libjpeg-turbo/lib/` in a folder, e.g. `/resource/x86libs/`. Then set the environment variables in x86 (absolute paths need to be provided below):
-
-    ```sh
-    export LD_LIBRARY_PATH=/resource/x86libs/:$LD_LIBRARY_PATH
-    ```
-
-3. After setting up the dependency environment, you can simulate starting multiple clients in the x86 environment for federated learning by referring to the application practice tutorial [Implementing an end-cloud federation for image classification application (x86)](https://www.mindspore.cn/federated/docs/en/master/image_classification_application.html).
-
-
diff --git a/docs/federated/docs/source_en/deploy_federated_server.md b/docs/federated/docs/source_en/deploy_federated_server.md
deleted file mode 100644
index 12bccc355a..0000000000
--- a/docs/federated/docs/source_en/deploy_federated_server.md
+++ /dev/null
@@ -1,317 +0,0 @@
-# Horizontal Federated Cloud-based Deployment
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/deploy_federated_server.md)
-
-The following uses LeNet as an example to describe how to use MindSpore Federated to deploy a horizontal federated learning cluster.
-
-The following figure shows the physical architecture of the MindSpore Federated Learning (FL) Server cluster:
-
-![mindspore-federated-networking](./images/mindspore_federated_networking.png)
-
-As shown in the preceding figure, in the horizontal federated learning cloud cluster, there are three MindSpore process roles: `Federated Learning Scheduler`, `Federated Learning Server` and `Federated Learning Worker`:
-
-- Federated Learning Scheduler
-
-    `Scheduler` provides the following functions:
-
-    1. Cluster networking assistance: During cluster initialization, the `Scheduler` collects server information and ensures cluster consistency.
-    2. Open management plane: You can manage clusters through the `RESTful` APIs.
-
-    In a federated learning task, there is only one `Scheduler`, which communicates with the `Server` using the TCP proprietary protocol.
-
-- Federated Learning Server
-
-    `Server` executes federated learning tasks, receives and parses data from devices, and provides capabilities such as secure aggregation, time-limited communication, and model storage. In a federated learning task, users can configure multiple `Servers` which communicate with each other through the TCP proprietary protocol and open HTTP ports for device-side connection.
-
-    In the MindSpore federated learning framework, `Server` also supports auto scaling and disaster recovery, and can dynamically schedule hardware resources without interrupting training tasks.
-
-- Federated Learning Worker
-
-    `Worker` is an accessory module for executing the federated learning task, which is used for supervised retraining of the model in the Server, and then the trained model is distributed to the Server. In a federated learning task, there can be more than one (user configurable) of `Worker`, and the communication between `Worker` and `Server` is performed via TCP protocol.
-
-`Scheduler` and `Server` must be deployed on a server or container with a single NIC and in the same network segment. MindSpore automatically obtains the first available IP address as the `Server` IP address.
-
-> The servers will verify the timestamp carried by the clients. It is necessary to eunsure the servers are periodically time synchronized to avoid a large time offset.
-
-## Preparations
-
-> Recommend to create a virtual environment for the following operations with [Anaconda](https://www.anaconda.com/).
-
-### Installing MindSpore
-
-The MindSpore horizontal federated learning cloud cluster supports deployment on x86 CPU and GPU CUDA hardware platforms. Run commands provided by the [MindSpore Installation Guide](https://www.mindspore.cn/install) to install the latest MindSpore.
-
-### Installing MindSpore Federated
-
-Compile and install with [source code](https://gitee.com/mindspore/federated).
-
-```shell
-git clone https://gitee.com/mindspore/federated.git -b master
-cd federated
-bash build.sh
-```
-
-For `bash build.sh`, compilation can be accelerated by the `-jn` option, e.g. `-j16`. The third-party dependencies can be downloaded from gitee instead of github by the `-S on` option.
-
-After compilation, find the whl installation package of Federated in the `build/package/` directory to install:
-
-```bash
-pip install mindspore_federated-{version}-{python_version}-linux_{arch}.whl
-```
-
-### Verifying Installation
-
-Execute the following command to verify the installation result. The installation is successful if no error is reported when importing Python modules.
-
-```python
-from mindspore_federated import FLServerJob
-```
-
-### Installing and Starting Redis Server
-
-Federated Learning relies on [Redis Server](https://gitee.com/link?target=https%3A%2F%2Fredis.io%2F) as the cached data middleware by default. To run the Federated Learning service, a Redis server needs to be installed and run.
-
-> User must check the security of the Redis to be used. Some versions may have security vulnerabilities.
-
-Install Redis server:
-
-```bash
-sudo apt-get install redis
-```
-
-Run the Redis server and the number of configuration side is 23456:
-
-```bash
-redis-server --port 23456 --save ""
-```
-
-## Starting a Cluster
-
-1. [examples](https://gitee.com/mindspore/federated/tree/master/example/cross_device_lenet_femnist/).
-
-    ```bash
-    cd example/cross_device_lenet_femnist
-    ```
-
-2. Modify the yaml configuration file according to the actual running: `default_yaml_config.yaml`. [sample configuration of Lenet](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/yamls/lenet/default_yaml_config.yaml) is as follows:
-
-    ```yaml
-    fl_name: Lenet
-    fl_iteration_num: 25
-    server_mode: FEDERATED_LEARNING
-    enable_ssl: False
-
-    distributed_cache:
-    type: redis
-    address: 127.0.0.1:23456 # ip:port of redis actual machine
-    plugin_lib_path: ""
-
-    round:
-    start_fl_job_threshold: 2
-    start_fl_job_time_window: 30000
-    update_model_ratio: 1.0
-    update_model_time_window: 30000
-    global_iteration_time_window: 60000
-
-    summary:
-    metrics_file: "metrics.json"
-    failure_event_file: "event.txt"
-    continuous_failure_times: 10
-    data_rate_dir: ".."
-    participation_time_level: "5,15"
-
-    unsupervised:
-      cluster_client_num: 1000
-      eval_type: SILHOUETTE_SCORE
-
-    encrypt:
-    encrypt_train_type: NOT_ENCRYPT
-    pw_encrypt:
-        share_secrets_ratio: 1.0
-        cipher_time_window: 3000
-        reconstruct_secrets_threshold: 1
-    dp_encrypt:
-        dp_eps: 50.0
-        dp_delta: 0.01
-        dp_norm_clip: 1.0
-    signds:
-        sign_k: 0.01
-        sign_eps: 100
-        sign_thr_ratio: 0.6
-        sign_global_lr: 0.1
-        sign_dim_out: 0
-
-    compression:
-    upload_compress_type: NO_COMPRESS
-    upload_sparse_rate: 0.4
-    download_compress_type: NO_COMPRESS
-
-    ssl:
-    # when ssl_config is set
-    # for tcp/http server
-    server_cert_path: "server.p12"
-    # for tcp client
-    client_cert_path: "client.p12"
-    # common
-    ca_cert_path: "ca.crt"
-    crl_path: ""
-    cipher_list: "ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-CHACHA20-POLY1305:ECDHE-PSK-CHACHA20-POLY1305:ECDHE-ECDSA-AES128-CCM:ECDHE-ECDSA-AES256-CCM:ECDHE-ECDSA-CHACHA20-POLY1305"
-    cert_expire_warning_time_in_day: 90
-
-    client_verify:
-    pki_verify: false
-    root_first_ca_path: ""
-    root_second_ca_path: ""
-    equip_crl_path: ""
-    replay_attack_time_diff: 600000
-
-    client:
-    http_url_prefix: ""
-    client_epoch_num: 20
-    client_batch_size: 32
-    client_learning_rate: 0.01
-    connection_num: 10000
-
-    ```
-
-3. Prepare the model file and start it in the following way: weight-based start. You need to provide the corresponding model weights.
-
-    Obtain lenet model weight:
-
-    ```bash
-    wget https://ms-release.obs.cn-north-4.myhuaweicloud.com/ms-dependencies/Lenet.ckpt
-    ```
-
-4. Run Scheduler, and the management side address is `127.0.0.1:11202` by default.
-
-    ```python
-    python run_sched.py \
-    --yaml_config="yamls/lenet.yaml" \
-    --scheduler_manage_address="10.*.*.*:18019"
-    ```
-
-5. Run Server, and start one Server and the HTTP server address is `127.0.0.1:6666` by default.
-
-    ```python
-    python run_server.py \
-    --yaml_config="yamls/lenet.yaml" \
-    --tcp_server_ip="10.*.*.*" \
-    --checkpoint_dir="fl_ckpt" \
-    --local_server_num=1 \
-    --http_server_address="10.*.*.*:8019"
-    ```
-
-6. Stop federated learning. The current version of the federated learning cluster is a resident process, and the `finish_cloud.py` script can be executed to terminate the federated learning service. The example of executing the command is as follows, where `redis_port` is passed with the same parameters as when starting redis, representing stopping the cluster corresponding to this `Scheduler`.
-
-    ```python
-    python finish_cloud.py --redis_port=23456
-    ```
-
-    If console prints the following contents:
-
-    ```text
-    killed $PID1
-    killed $PID2
-    killed $PID3
-    killed $PID4
-    killed $PID5
-    killed $PID6
-    killed $PID7
-    killed $PID8
-    ```
-
-    it indicates the termination service is successful.
-
-## Auto Scaling
-
-MindSpore federated learning framework supports `Server` auto scaling and provides `RESTful` services externally through the `Scheduler` management port, enabling users to dynamically schedule hardware resources without interrupting training tasks.
-
-The following example describes how to control scale-out and scale-in of cluster through APIs.
-
-### Scale-out
-
-After the cluster starts, enter the machine where the scheduler node is deployed and make a request to the `Scheduler` to query the status and node information. A `RESTful` request can be constructed with the `curl` command.
-
-```sh
-curl -k 'http://10.*.*.*:18015/state'
-```
-
-`Scheduler` will return query results in `json` format.
-
-```json
-{
-  "message":"Get cluster state successful.",
-  "cluster_state":"CLUSTER_READY",
-  "code":0,
-  "nodes":[
-    {"node_id","{ip}:{port}::{timestamp}::{random}",
-     "tcp_address":"{ip}:{port}",
-     "role":"SERVER"}
-  ]
-}
-```
-
-You need to pull up 3 new `Server` processes and accumulate the `local_server_num` parameter to the number of scale-out, so as to ensure the correctness of the global networking information, i.e. after scale-out, the number of `local_server_num` should be 4. An example of executing the command is as follows:
-
-```sh
-python run_server.py --yaml_config="yamls/lenet.yaml" --tcp_server_ip="10.*.*.*" --checkpoint_dir="fl_ckpt" --local_server_num=4 --http_server_address="10.*.*.*:18015"
-```
-
-This command indicates starting four `Server` nodes and the total number of `Server` is 4.
-
-### Scale-in
-
-Simulate the scale-in directly via kill -9 pid, construct a `RESTful` request with the `curl` command, and query the status, which finds that there is one node_id missing from the cluster to achieve the purpose of scale-in.
-
-```sh
-curl -k \
-'http://10.*.*.*:18015/state'
-```
-
-`Scheduler` returns the query results in `json` format.
-
-```json
-{
-  "message":"Get cluster state successful.",
-  "cluster_state":"CLUSTER_READY",
-  "code":0,
-  "nodes":[
-    {"node_id","{ip}:{port}::{timestamp}::{random}",
-     "tcp_address":"{ip}:{port}",
-     "role":"SERVER"},
-    {"node_id","worker_fl_{timestamp}::{random}",
-     "tcp_address":"",
-     "role":"WORKER"},
-    {"node_id","worker_fl_{timestamp}::{random}",
-     "tcp_address":"",
-     "role":"WORKER"}
-  ]
-}
-```
-
-> - After scale-out/scale-in of the cluster is successful, the training tasks are automatically resumed without additional intervention.
-
-## Security
-
-MindSpore federated Learning Framework supports SSL security authentication of `Server`. To enable security authentication, you need to add `enable_ssl=True` to the startup command, and the config.json configuration file specified by config_file_path needs to add the following fields:
-
-```json
-{
-    "server_cert_path": "server.p12",
-    "crl_path": "",
-    "client_cert_path": "client.p12",
-    "ca_cert_path": "ca.crt",
-    "cert_expire_warning_time_in_day": 90,
-    "cipher_list": "ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:kEDH+AESGCM:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES256-SHA:DHE-RSA-AES128-SHA256:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA:DHE-RSA-AES256-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!3DES:!MD5:!PSK",
-    "connection_num":10000
-}
-```
-
-- server_cert_path: The path to the p12 file containing the ciphertext of the certificate and key on the server-side.
-- crl_path: Files of revocation list.
-- client_cert_path: The path to the p12 file containing the ciphertext of the certificate and key on the client-side.
-- ca_cert_path: Root certificate.
-- cipher_list: Cipher suite.
-- cert_expire_warning_time_in_day: Alarm time of certificate expiration.
-
-The key in the p12 file is stored in cipher text.
diff --git a/docs/federated/docs/source_en/deploy_vfl.md b/docs/federated/docs/source_en/deploy_vfl.md
deleted file mode 100644
index 9aeae5961b..0000000000
--- a/docs/federated/docs/source_en/deploy_vfl.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Vertical Federated Deployment
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/deploy_vfl.md)
-
-This document explains how to use and deploy the vertical federated learning framework.
-
-The MindSpore Vertical Federated Learning (VFL) physical architecture is shown in the figure:
-
-![](./images/deploy_VFL_en.png)
-
-As shown above, there are two participants in the vertical federated interaction: the Leader node and the Follower node, each of which has processes in two roles: `FLDataWorker` and `VFLTrainer`:
-
-- FLDataWorker
-
-    The functions of `FLDataWorker` mainly includes:
-
-    1. Dataset intersection: obtains a common user intersection for both vertical federated participants, and supports a privacy dataset intersection protocol that prevents federated learning participants from obtaining ID information outside the intersection.
-    2. Training data generation: After obtaining the intersection ID, the data features are expanded to generate the mindrecord file for training.
-    3. Open management surface: `RESTful` interface is provided to users for cluster management.
-
-    In a federated learning task, there is only one `Scheduler`, which communicates with the `Server` through TCP protocol.
-
-- VFLTrainer
-
-    `VFLTrainer` is the main body that performs the vertical federated training tasks, and performs the forward and reverse computation after model slicing, Embedding tensor transfer, gradient tensor transfer, and reverse optimizer update. The current version supports single-computer single-card and single-computer multi-card training modes.
-
-    In the MindSpore federated learning framework, `Server` also supports elastic scaling and disaster recovery, enabling dynamic provisioning of hardware resources without interruption of training tasks.
-
-`FLDataWorker` and `VFLTrainer` are generally deployed in the same server or container.
-
-## Preparation
-
-> It is recommended to use [Anaconda](https://www.anaconda.com/) to create a virtual environment for the following operations.
-
-### Installing MindSpore
-
-MindSpore vertical federated supports deployment on x86 CPU, GPU CUDA and Ascend hardware platforms. The latest version of MindSpore can be installed by referring to [MindSpore Installation Guide](https://www.mindspore.cn/install).
-
-### Installing MindSpore Federated
-
-Compile and install via [source code](https://gitee.com/mindspore/federated).
-
-```shell
-git clone https://gitee.com/mindspore/federated.git -b master
-cd federated
-bash build.sh
-```
-
-For `bash build.sh`, accelerate compilation through the `-jn` option, e.g. `-j16`, and download third-party dependencies from gitee instead of github by the `-S on` option.
-
-Once compiled, find the Federated whl installation package in the `build/package/` directory to install.
-
-```shell
-pip install mindspore_federated-{version}-{python_version}-linux_{arch}.whl
-```
-
-#### Verifying installation
-
-Execute the following command to verify the installation. The installation is successful if no error is reported when importing Python modules.
-
-```python
-from mindspore_federated import FLServerJob
-```
-
-## Running the Example
-
-A running sample of FLDataWorker can be found in [Vertical federated learning data access](https://www.mindspore.cn/federated/docs/en/master/data_join.html).
-
-A running sample of VFLTrainer can be found in [Vertical federated learning model training - Wide&Deep Recommended Application](https://www.mindspore.cn/federated/docs/en/master/split_wnd_application.html).
diff --git a/docs/federated/docs/source_en/faq.md b/docs/federated/docs/source_en/faq.md
deleted file mode 100644
index 912e7d07d4..0000000000
--- a/docs/federated/docs/source_en/faq.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# FAQ
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/faq.md)
-
-**Q: If the cluster networking is unsuccessful, how to locate the cause?**
-
-A: Please check the server's network conditions, for example, check whether the firewall prohibits port access, please set the firewall to allow port access.
-
-
    
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/federated_install.md b/docs/federated/docs/source_en/federated_install.md
deleted file mode 100644
index 4a93f9e229..0000000000
--- a/docs/federated/docs/source_en/federated_install.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Obtaining MindSpore Federated
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/federated_install.md)
-
-Currently, the [MindSpore Federated](https://gitee.com/mindspore/federated) framework code has been built independently, divided into device-side and cloud-side. Its cloud-side capability relies on MindSpore and MindSpore Federated, using MindSpore for cloud-side cluster aggregation training and communication with device-side, so it needs to get MindSpore whl package and MindSpore Federated whl package respectively. The device-side capability relies on MindSpore Lite and MindSpore Federated java packages, where MindSpore Federated java is mainly responsible for data pre-processing, model training and inference by calling MindSpore Lite for, as well as model-related uploads and downloads by using privacy protection mechanisms and the cloud side.
-
-## Obtaining the MindSpore WHL Package
-
-You can use the source code or download the release version to install MindSpore on hardware platforms such as the x86 CPU and GPU CUDA. For details about the installation process, see [Install](https://www.mindspore.cn/install/en) on the MindSpore website.
-
-## Obtaining the MindSpore Lite Java Package
-
-You can use the source code or download the release version. Currently, only the Linux and Android platforms are supported, and only the CPU hardware architecture is supported. For details about the installation process, see [Downloading MindSpore Lite](https://www.mindspore.cn/lite/docs/en/master/use/downloads.html) and [Building MindSpore Lite](https://www.mindspore.cn/lite/docs/en/master/build/build.html).
-
-## Obtaining MindSpore Federated WHL Package
-
-You can use the source code or download the release version to install MindSpore on hardware platforms such as the x86 CPU and GPU CUDA. For details about the installation process, see [Building MindSpore Federated whl](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_server.html).
-
-## Obtaining MindSpore Federated Java Package
-
-You can use the source code or download the release version. Currently, MindSpore Federated Learing supports the Linux and Android platforms. For details about the installation process, see [Building MindSpore Federated java](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html).
-
-## Requirements for Building the Linux Environment
-
-Currently, the source code build is supported only in the Linux environment. For details about the environment requirements, see [MindSpore Source Code Build](https://www.mindspore.cn/install/en) and [MindSpore Lite Source Code Build](https://www.mindspore.cn/lite/docs/en/master/build/build.html).
diff --git a/docs/federated/docs/source_en/horizontal_server.rst b/docs/federated/docs/source_en/horizontal_server.rst
deleted file mode 100644
index bae616982a..0000000000
--- a/docs/federated/docs/source_en/horizontal_server.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-Federated Server
-================
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/horizontal_server.rst
-    :alt: View Source On Gitee
-
-.. toctree::
-   :maxdepth: 1
-
-   horizontal/federated_server
-   horizontal/federated_server_yaml
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/image_classfication_dataset_process.md b/docs/federated/docs/source_en/image_classfication_dataset_process.md
deleted file mode 100644
index 6bae8149f1..0000000000
--- a/docs/federated/docs/source_en/image_classfication_dataset_process.md
+++ /dev/null
@@ -1,450 +0,0 @@
-# Federated Learning Image Classification Dataset Process
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/image_classfication_dataset_process.md)
-
-This tutorial uses the federated learning dataset `FEMNIST` in the `leaf` dataset, which contains 62 different categories of handwritten digits and letters (digits 0 to 9, 26 lowercase letters, and 26 uppercase letters) with an image size of `28 x 28` pixels. The dataset contains handwritten digits and letters from 3500 users (up to 3500 clients can be simulated to participate in federated learning). The total data volume is 805,263, the average data volume per user is 226.83, and the variance of the data volume for all users is 88.94.
-
-Refer to [leaf dataset instruction](https://github.com/TalwalkarLab/leaf) to download the dataset.
-
-1. Environmental requirements before downloading the dataset.
-
-    ```sh
-    numpy==1.16.4
-    scipy                      # conda install scipy
-    tensorflow==1.13.1         # pip install tensorflow
-    Pillow                     # pip install Pillow
-    matplotlib                 # pip install matplotlib
-    jupyter                    # conda install jupyter notebook==5.7.8 tornado==4.5.3
-    pandas                     # pip install pandas
-    ```
-
-2. Use git to download the official dataset generation script.
-
-    ```sh
-    git clone https://github.com/TalwalkarLab/leaf.git
-    ```
-
-    After downloading the project, the directory structure is as follows:
-
-    ```sh
-    leaf/data/femnist
-        ├── data  # Used to store the dataset generated by the command
-        ├── preprocess  # Store the code related to data pre-processing
-        ├── preprocess.sh  # shell script generated by femnist dataset
-        └── README.md  # Official dataset download guidance
-    ```
-
-3. Taking `femnist` dataset as an example, run the following command to enter the specified path.
-
-    ```sh
-    cd  leaf/data/femnist
-    ```
-
-4. Using the command `./preprocess.sh -s niid --sf 1.0 -k 0 -t sample` generates a dataset containing 3500 users, and the training sets and the test sets are divided in a ratio of 9:1 for each user's data.
-
-    The meaning of the parameters in the command can be found in the `leaf/data/femnist/README.md` file.
-
-    The directory structure after running is as follows:
-
-    ```text
-    leaf/data/femnist/35_client_sf1_data/
-        ├── all_data  # All datasets are mixed together, without distinguishing the training sets and test sets, containing a total of 35 json files, and each json file contains the data of 100 users
-        ├── test  # The test sets are divided into the training sets and the test sets in a ratio of 9:1 for each user's data, containing a total of 35 json files, and each json file contains the data of 100 users
-        ├── train  # The training sets are divided into the training sets and the test sets in a ratio of 9:1 for each user's data, containing a total of 35 json files, and each json file contains the data of 100 users
-        └── ...  # Other documents do not need to use, and details are not described herein
-    ```
-
-    Each json file contains the following three parts:
-
-    - `users`: User list.
-    - `num_samples`: The sample number list of each user.
-    - `user_data`: A dictionary object with user names as key and their respective data as value. For each user, the data is represented as a list of images, with each image represented as a list of integers of size 784 (obtained by spreading the `28 x 28` image array).
-
-    Before rerunning `preprocess.sh`, make sure to delete the `rem_user_data`, `sampled_data`, `test` and `train` subfolders from the data directory.
-
-5. Divide the 35 json files into 3500 json files (each json file represents a user).
-
-    The code is as follows:
-
-    ```python
-    import os
-    import json
-
-    def mkdir(path):
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-    def partition_json(root_path, new_root_path):
-        """
-        partition 35 json files to 3500 json file
-
-        Each raw .json file is an object with 3 keys:
-        1. 'users', a list of users
-        2. 'num_samples', a list of the number of samples for each user
-        3. 'user_data', an object with user names as keys and their respective data as values; for each user, data is represented as a list of images, with each image represented as a size-784 integer list (flattened from 28 by 28)
-
-        Each new .json file is an object with 3 keys:
-        1. 'user_name', the name of user
-        2. 'num_samples', the number of samples for the user
-        3. 'user_data', an dict object with 'x' as keys and their respective data as values; with 'y' as keys and their respective label as values;
-
-        Args:
-            root_path (str): raw root path of 35 json files
-            new_root_path (str): new root path of 3500 json files
-        """
-        paths = os.listdir(root_path)
-        count = 0
-        file_num = 0
-        for i in paths:
-            file_num += 1
-            file_path = os.path.join(root_path, i)
-            print('======== process ' + str(file_num) + ' file: ' + str(file_path) + '======================')
-            with open(file_path, 'r') as load_f:
-                load_dict = json.load(load_f)
-                users = load_dict['users']
-                num_users = len(users)
-                num_samples = load_dict['num_samples']
-                for j in range(num_users):
-                    count += 1
-                    print('---processing user: ' + str(count) + '---')
-                    cur_out = {'user_name': None, 'num_samples': None, 'user_data': {}}
-                    cur_user_id = users[j]
-                    cur_data_num = num_samples[j]
-                    cur_user_path = os.path.join(new_root_path, cur_user_id + '.json')
-                    cur_out['user_name'] = cur_user_id
-                    cur_out['num_samples'] = cur_data_num
-                    cur_out['user_data'].update(load_dict['user_data'][cur_user_id])
-                    with open(cur_user_path, 'w') as f:
-                        json.dump(cur_out, f)
-        f = os.listdir(new_root_path)
-        print(len(f), ' users have been processed!')
-    # partition train json files
-    partition_json("leaf/data/femnist/35_client_sf1_data/train", "leaf/data/femnist/3500_client_json/train")
-    # partition test json files
-    partition_json("leaf/data/femnist/35_client_sf1_data/test", "leaf/data/femnist/3500_client_json/test")
-    ```
-
-    where `root_path` is `leaf/data/femnist/35_client_sf1_data/{train,test}`. `new_root_path` is set by itself to store the generated 3500 user json files, which need to be processed separately for the training and test folders.
-
-    Each of the 3500 newly generated user json files contains the following three parts:
-
-    - `user_name`: User name.
-    - `num_samples`: The number of user samples
-    - `user_data`: A dictionary object with 'x' as key and user data as value; with 'y' as key and the label corresponding to the user data as value.
-
-    Print the result as following after running the script, which means a successful run:
-
-    ```sh
-    ======== process 1 file: /leaf/data/femnist/35_client_sf1_data/train/all_data_16_niid_0_keep_0_train_9.json======================
-    ---processing user: 1---
-    ---processing user: 2---
-    ---processing user: 3---
-    ......
-    ```
-
-6. Convert a json file to an image file.
-
-    Refer to the following code:
-
-    ```python
-    import os
-    import json
-    import numpy as np
-    from PIL import Image
-
-    name_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
-                 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U',
-                 'V', 'W', 'X', 'Y', 'Z',
-                 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',
-                 'v', 'w', 'x', 'y', 'z'
-                 ]
-
-    def mkdir(path):
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-    def json_2_numpy(img_size, file_path):
-        """
-        read json file to numpy
-        Args:
-            img_size (list): contain three elements: the height, width, channel of image
-            file_path (str): root path of 3500 json files
-        return:
-            image_numpy (numpy)
-            label_numpy (numpy)
-        """
-        # open json file
-        with open(file_path, 'r') as load_f_train:
-            load_dict = json.load(load_f_train)
-            num_samples = load_dict['num_samples']
-            x = load_dict['user_data']['x']
-            y = load_dict['user_data']['y']
-            size = (num_samples, img_size[0], img_size[1], img_size[2])
-            image_numpy = np.array(x, dtype=np.float32).reshape(size)  # mindspore doesn't support float64 and int64
-            label_numpy = np.array(y, dtype=np.int32)
-        return image_numpy, label_numpy
-
-    def json_2_img(json_path, save_path):
-        """
-        transform single json file to images
-
-        Args:
-            json_path (str): the path json file
-            save_path (str): the root path to save images
-
-        """
-        data, label = json_2_numpy([28, 28, 1], json_path)
-        for i in range(data.shape[0]):
-            img = data[i] * 255  # PIL don't support the 0/1 image ,need convert to 0~255 image
-            im = Image.fromarray(np.squeeze(img))
-            im = im.convert('L')
-            img_name = str(label[i]) + '_' + name_list[label[i]] + '_' + str(i) + '.png'
-            path1 = os.path.join(save_path, str(label[i]))
-            mkdir(path1)
-            img_path = os.path.join(path1, img_name)
-            im.save(img_path)
-            print('-----', i, '-----')
-
-    def all_json_2_img(root_path, save_root_path):
-        """
-        transform json files to images
-        Args:
-            json_path (str): the root path of 3500 json files
-            save_path (str): the root path to save images
-        """
-        usage = ['train', 'test']
-        for i in range(2):
-            x = usage[i]
-            files_path = os.path.join(root_path, x)
-            files = os.listdir(files_path)
-
-            for name in files:
-                user_name = name.split('.')[0]
-                json_path = os.path.join(files_path, name)
-                save_path1 = os.path.join(save_root_path, user_name)
-                mkdir(save_path1)
-                save_path = os.path.join(save_path1, x)
-                mkdir(save_path)
-                print('=============================' + name + '=======================')
-                json_2_img(json_path, save_path)
-
-    all_json_2_img("leaf/data/femnist/3500_client_json/", "leaf/data/femnist/3500_client_img/")
-    ```
-
-    Print the result as following after running the script, which means a successful run:
-
-    ```sh
-    =============================f0644_19.json=======================
-    ----- 0 -----
-    ----- 1 -----
-    ----- 2 -----
-    ......
-    ```
-
-7. Since the dataset under some user folders is small, if the number is smaller than the batch size, random expansion is required.
-
-    The entire dataset `"leaf/data/femnist/3500_client_img/"` can be checked and expanded by referring to the following code:
-
-    ```python
-    import os
-    import shutil
-    from random import choice
-
-    def count_dir(path):
-        num = 0
-        for root, dirs, files in os.walk(path):
-            for file in files:
-                num += 1
-        return num
-
-    def get_img_list(path):
-        img_path_list = []
-        label_list = os.listdir(path)
-        for i in range(len(label_list)):
-            label = label_list[i]
-            imgs_path = os.path.join(path, label)
-            imgs_name = os.listdir(imgs_path)
-            for j in range(len(imgs_name)):
-                img_name = imgs_name[j]
-                img_path = os.path.join(imgs_path, img_name)
-                img_path_list.append(img_path)
-        return img_path_list
-
-    def data_aug(data_root_path, batch_size = 32):
-        users = os.listdir(data_root_path)
-        tags = ["train", "test"]
-        aug_users = []
-        for i in range(len(users)):
-            user = users[i]
-            for tag in tags:
-                data_path = os.path.join(data_root_path, user, tag)
-                num_data = count_dir(data_path)
-                if num_data < batch_size:
-                    aug_users.append(user + "_" + tag)
-                    print("user: ", user, " ", tag, " data number: ", num_data, " < ", batch_size, " should be aug")
-                    aug_num = batch_size - num_data
-                    img_path_list = get_img_list(data_path)
-                    for j in range(aug_num):
-                        img_path = choice(img_path_list)
-                        info = img_path.split(".")
-                        aug_img_path = info[0] + "_aug_" + str(j) + ".png"
-                        shutil.copy(img_path, aug_img_path)
-                        print("[aug", j, "]", "============= copy file:", img_path, "to ->", aug_img_path)
-        print("the number of all aug users: " + str(len(aug_users)))
-        print("aug user name: ", end=" ")
-        for k in range(len(aug_users)):
-            print(aug_users[k], end = " ")
-
-    if __name__ == "__main__":
-        data_root_path = "leaf/data/femnist/3500_client_img/"
-        batch_size = 32
-        data_aug(data_root_path,  batch_size)
-    ```
-
-8. Convert the expanded image dataset into a bin file format usable in the Federated Learning Framework.
-
-    Refer to the following code:
-
-    ```python
-    import numpy as np
-    import os
-    import mindspore.dataset as ds
-    import mindspore.dataset.vision as vision
-    import mindspore.dataset.transforms as transforms
-    import mindspore
-
-    def mkdir(path):
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-    def count_id(path):
-        files = os.listdir(path)
-        ids = {}
-        for i in files:
-            ids[i] = int(i)
-        return ids
-
-    def create_dataset_from_folder(data_path, img_size, batch_size=32, repeat_size=1, num_parallel_workers=1, shuffle=False):
-        """ create dataset for train or test
-            Args:
-                data_path: Data path
-                batch_size: The number of data records in each group
-                repeat_size: The number of replicated data records
-                num_parallel_workers: The number of parallel workers
-            """
-        # define dataset
-        ids = count_id(data_path)
-        mnist_ds = ds.ImageFolderDataset(dataset_dir=data_path, decode=False, class_indexing=ids)
-        # define operation parameters
-        resize_height, resize_width = img_size[0], img_size[1]  # 32
-
-        transform = [
-            vision.Decode(True),
-            vision.Grayscale(1),
-            vision.Resize(size=(resize_height, resize_width)),
-            vision.Grayscale(3),
-            vision.ToTensor(),
-        ]
-        compose = transforms.Compose(transform)
-
-        # apply map operations on images
-        mnist_ds = mnist_ds.map(input_columns="label", operations=transforms.TypeCast(mindspore.int32))
-        mnist_ds = mnist_ds.map(input_columns="image", operations=compose)
-
-        # apply DatasetOps
-        buffer_size = 10000
-        if shuffle:
-            mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
-        mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
-        mnist_ds = mnist_ds.repeat(repeat_size)
-        return mnist_ds
-
-    def img2bin(root_path, root_save):
-        """
-        transform images to bin files
-
-        Args:
-        root_path: the root path of 3500 images files
-        root_save: the root path to save bin files
-
-        """
-
-        use_list = []
-        train_batch_num = []
-        test_batch_num = []
-        mkdir(root_save)
-        users = os.listdir(root_path)
-        for user in users:
-            use_list.append(user)
-            user_path = os.path.join(root_path, user)
-            train_test = os.listdir(user_path)
-            for tag in train_test:
-                data_path = os.path.join(user_path, tag)
-                dataset = create_dataset_from_folder(data_path, (32, 32, 1), 32)
-                batch_num = 0
-                img_list = []
-                label_list = []
-                for data in dataset.create_dict_iterator():
-                    batch_x_tensor = data['image']
-                    batch_y_tensor = data['label']
-                    trans_img = np.transpose(batch_x_tensor.asnumpy(), [0, 2, 3, 1])
-                    img_list.append(trans_img)
-                    label_list.append(batch_y_tensor.asnumpy())
-                    batch_num += 1
-
-                if tag == "train":
-                    train_batch_num.append(batch_num)
-                elif tag == "test":
-                    test_batch_num.append(batch_num)
-
-                imgs = np.array(img_list)  # (batch_num, 32,3,32,32)
-                labels = np.array(label_list)
-                path1 = os.path.join(root_save, user)
-                mkdir(path1)
-                image_path = os.path.join(path1, user + "_" + "bn_" + str(batch_num) + "_" + tag + "_data.bin")
-                label_path = os.path.join(path1, user + "_" + "bn_" + str(batch_num) + "_" + tag + "_label.bin")
-
-                imgs.tofile(image_path)
-                labels.tofile(label_path)
-                print("user: " + user + " " + tag + "_batch_num: " + str(batch_num))
-        print("total " + str(len(use_list)) + " users finished!")
-
-    root_path = "leaf/data/femnist/3500_client_img/"
-    root_save = "leaf/data/femnist/3500_clients_bin"
-    img2bin(root_path, root_save)
-    ```
-
-    Print the result as following after running the script, which means a successful run:
-
-    ```sh
-    user: f0141_43 test_batch_num: 1
-    user: f0141_43 train_batch_num: 10
-    user: f0137_14 test_batch_num: 1
-    user: f0137_14 train_batch_num: 11
-    ......
-    total 3500 users finished!
-    ```
-
-9. Generate `3500_clients_bin` folder containing a total of 3500 user folders with the following directory structure:
-
-    ```sh
-    leaf/data/femnist/3500_clients_bin
-      ├── f0000_14  # User number
-      │   ├── f0000_14_bn_10_train_data.bin  # The training data of user f0000_14 (The number 10 after bn_ represents the batch number)
-      │   ├── f0000_14_bn_10_train_label.bin  # Training tag for user f0000_14
-      │   ├── f0000_14_bn_1_test_data.bin  # Test data of user f0000_14 (the number 1 after bn_ represents batch number)
-      │   └── f0000_14_bn_1_test_label.bin  # Test tag for user f0000_14
-      ├── f0001_41  # User number
-      │   ├── f0001_41_bn_11_train_data.bin  # The training data of user f0001_41 (The number 11 after bn_ represents the batch number)
-      │   ├── f0001_41_bn_11_train_label.bin  # Training tag for user f0001_41
-      │   ├── f0001_41_bn_1_test_data.bin  # Test data of user f0001_41 (the number 1 after bn_ represents batch number)
-      │   └── f0001_41_bn_1_test_label.bin  # Test tag for user f0001_41
-      │                    ...
-      └── f4099_10  # User number
-          ├── f4099_10_bn_4_train_data.bin  # The training data of user f4099_10 (the number 4 after bn_ represents the batch number)
-          ├── f4099_10_bn_4_train_label.bin  # Training tag for user f4099_10
-          ├── f4099_10_bn_1_test_data.bin  # Test data of user f4099_10 (the number 1 after bn_ represents batch number)
-          └── f4099_10_bn_1_test_label.bin  # Test tag for user f4099_10
-    ```
-
-The `3500_clients_bin` folder generated according to steps 1 to 9 above can be directly used as the input data for the device-cloud federated image classification task.
diff --git a/docs/federated/docs/source_en/image_classification_application.md b/docs/federated/docs/source_en/image_classification_application.md
deleted file mode 100644
index 04a7b00dc0..0000000000
--- a/docs/federated/docs/source_en/image_classification_application.md
+++ /dev/null
@@ -1,331 +0,0 @@
-# Implementing an Image Classification Application of Cross-device Federated Learning (x86)
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/image_classification_application.md)
-
-Federated learning can be divided into cross-silo federated learning and cross-device federated learning according to different participating clients. In the cross-silo federated learning scenario, the clients participating in federated learning are different organizations (for example, medical or financial) or data centers geographically distributed, that is, training models on multiple data islands. The clients participating in the cross-device federated learning scenario are a large number of mobiles or IoT devices. This framework will introduce how to use the network LeNet to implement an image classification application on the MindSpore cross-silo federated framework, and provides related tutorials for simulating to start multi-client participation in federated learning in the x86 environment.
-
-Before you start, check whether MindSpore has been correctly installed. If not, install MindSpore on your computer by referring to [Install](https://www.mindspore.cn/install/en) on the MindSpore website.
-
-## Preparation
-
-We provide [Federated Learning Image Classification Dataset FEMNIST](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/federated/3500_clients_bin.zip) and the [device-side model file](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/lenet_train.ms) of the `.ms` format for users to use directly. Users can also refer to the following tutorials to generate the datasets and models based on actual needs.
-
-### Generating a Device-side Model File
-
-1. Define the network and training process.
-
-    For the definition of the specific network and training process, please refer to [Beginners Getting Started](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html).
-
-2. Export a model as a MindIR file.
-
-    The code snippet is as follows:
-
-    ```python
-    import argparse
-    import numpy as np
-    import mindspore as ms
-    import mindspore.nn as nn
-
-    def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-        """weight initial for conv layer"""
-        weight = weight_variable()
-        return nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            weight_init=weight,
-            has_bias=False,
-            pad_mode="valid",
-        )
-
-    def fc_with_initialize(input_channels, out_channels):
-        """weight initial for fc layer"""
-        weight = weight_variable()
-        bias = weight_variable()
-        return nn.Dense(input_channels, out_channels, weight, bias)
-
-    def weight_variable():
-        """weight initial"""
-        return ms.common.initializer.TruncatedNormal(0.02)
-
-    class LeNet5(nn.Cell):
-        def __init__(self, num_class=10, channel=3):
-            super(LeNet5, self).__init__()
-            self.num_class = num_class
-            self.conv1 = conv(channel, 6, 5)
-            self.conv2 = conv(6, 16, 5)
-            self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-            self.fc2 = fc_with_initialize(120, 84)
-            self.fc3 = fc_with_initialize(84, self.num_class)
-            self.relu = nn.ReLU()
-            self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.flatten = nn.Flatten()
-
-        def construct(self, x):
-            x = self.conv1(x)
-            x = self.relu(x)
-            x = self.max_pool2d(x)
-            x = self.conv2(x)
-            x = self.relu(x)
-            x = self.max_pool2d(x)
-            x = self.flatten(x)
-            x = self.fc1(x)
-            x = self.relu(x)
-            x = self.fc2(x)
-            x = self.relu(x)
-            x = self.fc3(x)
-            return x
-
-    parser = argparse.ArgumentParser(description="export mindir for lenet")
-    parser.add_argument("--device_target", type=str, default="CPU")
-    parser.add_argument("--mindir_path", type=str,
-                        default="lenet_train.mindir")  # the mindir file path of the model to be export
-
-    args, _ = parser.parse_known_args()
-    device_target = args.device_target
-    mindir_path = args.mindir_path
-
-    ms.set_context(mode=ms.GRAPH_MODE, device_target=device_target)
-
-    if __name__ == "__main__":
-        np.random.seed(0)
-        network = LeNet5(62)
-        criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=False, reduction="mean")
-        net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
-        net_with_criterion = nn.WithLossCell(network, criterion)
-        train_network = nn.TrainOneStepCell(net_with_criterion, net_opt)
-        train_network.set_train()
-
-        data = ms.Tensor(np.random.rand(32, 3, 32, 32).astype(np.float32))
-        label = ms.Tensor(np.random.randint(0, 1, (32, 62)).astype(np.float32))
-        ms.export(train_network, data, label, file_name=mindir_path,
-                  file_format='MINDIR')  # Add the export statement to obtain the model file in MindIR format.
-    ```
-
-    The parameter `--mindir_path` is used to set the path of the generated file in MindIR format.
-
-3. Convert the MindIR file into an .ms file that can be used by the federated learning device-side framework.
-
-    For details about model conversion, see [Training Model Conversion Tutorial](https://www.mindspore.cn/lite/docs/en/master/train/converter_train.html).
-
-    The following is an example of model conversion:
-
-    Assume that the model file to be converted is `lenet_train.mindir`. Run the following command:
-
-    ```sh
-    ./converter_lite --fmk=MINDIR --trainModel=true --modelFile=lenet_train.mindir --outputFile=lenet_train
-    ```
-
-    If the conversion is successful, the following information is displayed:
-
-    ```sh
-    CONVERT RESULT SUCCESS:0
-    ```
-
-    This indicates that the MindSpore model is successfully converted to the MindSpore device-side model and the new file `lenet_train.ms` is generated. If the conversion fails, the following information is displayed:
-
-    ```sh
-    CONVERT RESULT FAILED:
-    ```
-
-    The generated model file in `.ms` format is the model file required by subsequent clients.
-
-## Simulating Multi-client Participation in Federated Learning
-
-### Preparing a Model File for the Client
-
-This example uses lenet on the device-side to simulate the actual network used, where[device-side model file](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/lenet_train.ms) in `.ms` format of lenet. As the real scenario where a client contains only one model file in .ms format, in the simulation scenario, multiple copies of the .ms file need to be copied and named according to the `lenet_train{i}.ms` format, where i represents the client number, since the .ms file has been automatically copied for each client in `run_client_x86.py`.
-
-See the copy_ms function in [startup script](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/simulate_x86/run_client_x86.py) for details.
-
-### Starting the Cloud Side Service
-
-Users can first refer to [cloud-side deployment tutorial](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_server.html) to deploy the cloud-side environment and start the cloud-side service.
-
-### Starting the Client
-
-Before starting the client, please refer to the section [Device-side deployment tutotial](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html) for deployment of device environment.
-
-We provide a reference script [run_client_x86.py](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/simulate_x86/run_client_x86.py), users can set relevant parameters to start different federated learning interfaces.
-After the cloud-side service is successfully started, the script providing run_client_x86.py is used to call the federated learning framework jar package `mindspore-lite-java-flclient.jar` and the corresponding jar package `quick_start_flclient.jar` of the model script, obtaining in [Compiling package Flow in device-side deployment](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html) to simulate starting multiple clients to participate in the federated learning task.
-
-Taking the LeNet network as an example, some of the input parameters in the `run_client_x86.py` script have the following meanings, and users can set them according to the actual situation:
-
-- `--fl_jar_path`
-
-    For setting the federated learning jar package path and obtaining x86 environment federated learning jar package, refer to [Compile package process in device-side deployment](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html).
-
-- `--case_jar_path`
-
-    For setting the path of jar package `quick_start_flclient.jar` generated by model script and obtaining the JAR package in the x86 environment, see [Compile package process in device-side deployment](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html).
-
-- `--lite_jar_path`
-
-    For setting the path of jar package `mindspore-lite-java.jar` of mindspore lite, which is located in `mindspore-lite-{version}-linux-x64.tar.gz`. For x86 environment federated learning jar package acquisition, see [Compile package process in device-side deployment](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html).
-
-- `--train_data_dir`
-
-    The root path of the training dataset in which the LeNet image classification task is stored is the training data.bin file and label.bin file for each client, e.g. `data/femnist/3500_clients_bin/`.
-
-- `--fl_name`
-
-    Specifies the package path of model script used by federated learning. We provide two types of model scripts for your reference ([Supervised sentiment classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert), [Lenet image classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet)). For supervised sentiment classification tasks, this parameter can be set to the package path of the provided script file [AlBertClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert/AlbertClient.java), like as `com.mindspore.flclient.demo.albert.AlbertClient`. For Lenet image classification tasks, this parameter can be set to the package path of the provided script file [LenetClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet/LenetClient.java), like as `com.mindspore.flclient.demo.lenet.LenetClient`. At the same time, users can refer to these two types of model scripts, define the model script by themselves, and then set the parameter to the package path of the customized model file ModelClient.java (which needs to inherit from the class [Client.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/model/Client.java)).
-
-- `--train_model_dir`
-
-    Specifies the training model path used for federated learning. The path is the directory where multiple .ms files copied in the preceding tutorial are stored, for example, `ms/lenet`. The path must be an absolute path.
-
-- `--domain_name`
-
-    Used to set the url for device-cloud communication. Currently, https and http communication are supported, and the corresponding formats are like as: https://......, http://....... When `if_use_elb` is set to true, the format must be:  or , where `127.0.0.1` corresponds to the ip of the machine ip providing cloud-side services (corresponding to the cloud-side parameter `--scheduler_ip`), and `6666` corresponds to the cloud-side parameter `--fl_server_port`.
-
-    Note 1: When this parameter is set to `http://......`, it means that HTTP communication is used, and there may be communication security risks.
-
-    Note 2: When this parameter is set to `https://......`, it means the use of HTTPS communication. At this time, SSL certificate authentication must be performed, and the certificate path needs to be set by the parameter `-cert_path`.
-
-- `--task`
-
-    Specifies the type of the task to be started. `train` indicates that a training task is started. `inference` indicates that multiple data inference tasks are started. `getModel` indicates that the task for obtaining the cloud model is started. Other character strings indicate that the inference task of a single data record is started. The default value is `train`. The initial model file (.ms file) is not trained. Therefore, you are advised to start the training task first. After the training is complete, start the inference task. (Note that the values of client_num in the two startups must be the same to ensure that the model file used by `inference` is the same as that used by `train`.)
-
-- `--batch_size`
-
-    Specifies the number of single-step training samples used in federated learning training and inference, that is, batch size. It needs to be consistent with the batch size of the input data of the model.
-
-- `--client_num`
-
-    Specifies the number of clients. The value must be the same as that of `start_fl_job_cnt` when the server is started. This parameter is not required in actual scenarios.
-
-If you want to know more about the meaning of other parameters in the `run_client_x86.py` script, you can refer to the comments in the script.
-
-The basic startup instructions of the federated learning interface are as follows:
-
-```sh
- rm -rf client_*\
- && rm -rf ms/* \
- && python3 run_client_x86.py \
- --fl_jar_path="federated/mindspore_federated/device_client/build/libs/jarX86/mindspore-lite-java-flclient.jar" \
- --case_jar_path="federated/example/quick_start_flclient/target/case_jar/quick_start_flclient.jar" \
-  --lite_jar_path="federated/mindspore_federated/device_client/third/mindspore-lite-2.0.0-linux-x64/runtime/lib/mindspore-lite-java.jar" \
- --train_data_dir="federated/tests/st/simulate_x86/data/3500_clients_bin/" \
- --eval_data_dir="null" \
- --infer_data_dir="null" \
- --vocab_path="null" \
- --ids_path="null" \
- --path_regex="," \
- --fl_name="com.mindspore.flclient.demo.lenet.LenetClient" \
- --origin_train_model_path="federated/tests/st/simulate_x86/ms_files/lenet/lenet_train.ms" \
- --origin_infer_model_path="null" \
- --train_model_dir="ms" \
- --infer_model_dir="ms" \
- --ssl_protocol="TLSv1.2" \
- --deploy_env="x86" \
- --domain_name="http://10.*.*.*:8010" \
- --cert_path="CARoot.pem" --use_elb="false" \
- --server_num=1 \
- --task="train" \
- --thread_num=1 \
- --cpu_bind_mode="NOT_BINDING_CORE" \
- --train_weight_name="null" \
- --infer_weight_name="null" \
- --name_regex="::" \
- --server_mode="FEDERATED_LEARNING" \
- --batch_size=32 \
- --input_shape="null" \
- --client_num=8
-```
-
-Note that the related path in the startup command must give an absolute path.
-
-The above commands indicate that eight clients are started to participate in federated learning. If the startup is successful, log files corresponding to the eight clients are generated in the current folder. You can view the log files to learn the running status of each client:
-
-```text
-./
-├── client_0
-│   └── client.log  # Log file of client 0.
-│           ......
-└── client_7
-    └── client.log  # Log file of client 7.
-```
-
-For different interfaces and scenarios, you only need to modify specific parameter values according to the meaning of the parameters, such as:
-
-- Start federated learning and training tasks: SyncFLJob.flJobRun()
-
-    When `--task` in `Basic Start Command` is set to `train`, it means to start the task.
-
-    You can use the command `grep -r "average loss:" client_0/client.log` to view the average loss of each epoch of `client_0` during the training process. It will be printed as follows:
-
-    ```sh
-    INFO:  ----------epoch:0,average loss:4.1258564 ----------
-    ......
-    ```
-
-    You can also use the command `grep -r "evaluate acc:" client_0/client.log` to view the verification accuracy of the model after the aggregation in each federated learning iteration for `client_0` . It will be printed like the following:
-
-    ```sh
-    INFO:  [evaluate] evaluate acc: 0.125
-    ......
-    ```
-
-    On the cloud side, the number of client group ids and algorithm type for unsupervised cluster index statistics can be specified by setting the 'cluster_client_num' parameter and 'eval_type' parameter of yaml configuration file. The 'metrics.json' statistical file generated on the cloud side can query the unsupervised indicator information:
-
-    ```text
-    "unsupervisedEval":0.640
-    "unsupervisedEval":0.675
-    "unsupervisedEval":0.677
-    "unsupervisedEval":0.706
-    ......
-    ```
-
-- Start the inference task: SyncFLJob.modelInference()
-
-    When `--task` in `Basic Start Command` is set to `inference`, it means to start the task.
-
-    You can view the inference result of `client_0` through the command `grep -r "the predicted labels:" client_0/client.log`:
-
-    ```sh
-    INFO:  [model inference] the predicted labels: [0, 0, 0, 1, 1, 1, 2, 2, 2]
-    ......
-    ```
-
-- Start the task of obtaining the latest model on the cloud side: SyncFLJob.getModel()
-
-    When `--task` in `Basic Start Command` is set to `inference`, it means to start the task.
-
-    If there is the following content in the log file, it means that the latest model on the cloud side is successfully obtained:
-
-    ```sh
-    INFO:  [getModel] get response from server ok!
-    ```
-
-### Stopping the Client Process
-
-For details, see the [finish.py](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/simulate_x86/finish.py) script. The details are as follows:
-
-The command of stopping the client process:
-
-```sh
-python finish.py --kill_tag=mindspore-lite-java-flclient
-```
-
-The parameter `--kill_tag` is used to search for the keyword to kill the client process. You only need to set the special keyword in `--jarPath`. The default value is `mindspore-lite-java-flclient`, that is, the name of the federated learning JAR package. The user can check whether the process still exists through the command `ps -ef |grep "mindspore-lite-java-flclient"`.
-
-Experimental results of 50 clients participating in federated learning and training tasks.
-
-Currently, the `3500_clients_bin` folder contains data of 3500 clients. This script can simulate a maximum of 3500 clients to participate in federated learning.
-
-The following figure shows the accuracy of the test dataset for federated learning on 50 clients (set `server_num` to 16).
-
-![lenet_50_clients_acc](images/lenet_50_clients_acc_en.png)
-
-The total number of federated learning iterations is 100, the number of epochs for local training on the client is 20, and the value of batchSize is 32.
-
-The test accuracy in the figure refers to the accuracy of each client test dataset on the aggregated model on the cloud for each federated learning iteration:
-
-AVG: average accuracy of 50 clients in the test dataset for each federated learning iteration.
-
-TOP5: average accuracy of the 5 clients with the highest accuracy in the test dataset for each federated learning iteration.
-
-LOW5: average accuracy of the 5 clients with the lowest accuracy in the test dataset for each federated learning iteration.
diff --git a/docs/federated/docs/source_en/image_classification_application_in_cross_silo.md b/docs/federated/docs/source_en/image_classification_application_in_cross_silo.md
deleted file mode 100644
index 86196f4866..0000000000
--- a/docs/federated/docs/source_en/image_classification_application_in_cross_silo.md
+++ /dev/null
@@ -1,313 +0,0 @@
-# Implementing a Cloud-Slio Federated Image Classification Application (x86)
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/image_classification_application_in_cross_silo.md)
-
-Based on the type of participating clients, federated learning can be classified into cross-silo federated learning and cross-device federated learning. In a cross-silo federated learning scenario, the clients involved in federated learning are different organizations (e.g., healthcare or finance) or geographically distributed data centers, i.e., training models on multiple data silos. In the cross-device federated learning scenario, the participating clients are a large number of mobile or IoT devices. This framework will describe how to implement an image classification application by using the network LeNet on the MindSpore Federated cross-silo federated framework.
-
-The full script to launch cross-silo federated image classification application can be found [here](https://gitee.com/mindspore/federated/tree/master/example/cross_silo_femnist).
-
-## Downloading the Dataset
-
-This example uses the federated learning dataset `FEMNIST` from [leaf dataset](https://github.com/TalwalkarLab/leaf), which contains 62 different categories of handwritten numbers and letters (numbers 0 to 9, 26 lowercase letters, 26 uppercase letters) with an image size of `28 x 28` pixels. The dataset contains handwritten digits and letters from 3500 users (up to 3500 clients can be simulated to participate in federated learning). The total data volume is 805263, the average amount of data contained per user is 226.83, and the variance of the data volume for all users is 88.94.
-
-You can refer to [Image classfication dataset process](https://www.mindspore.cn/federated/docs/en/master/image_classfication_dataset_process.html) in steps 1 to 7 to obtain the 3500 user datasets `3500_client_img` in the form of images.
-
-Due to the relatively small amount of data per user in the original 3500 user dataset, it will converge too fast in the cross-silo federated task to obviously reflect the convergence effect of the cross-silo federated framework. The following provides a reference script to merge the specified number of user data into one user to increase the amount of individual user data participating in the cross-silo federated task and better simulate the cross-silo federated framework experiment.
-
-```python
-import os
-import shutil
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.mkdir(path)
-
-
-def combine_users(root_data_path, new_data_path, raw_user_num, new_user_num):
-    mkdir(new_data_path)
-    user_list = os.listdir(root_data_path)
-    num_per_user = int(raw_user_num / new_user_num)
-    for i in range(new_user_num):
-        print(
-            "========================== combine the raw {}~{} users to the new user: dataset_{} ==========================".format(
-                i * num_per_user, i * num_per_user + num_per_user - 1, i))
-        new_user = "dataset_" + str(i)
-        new_user_path = os.path.join(new_data_path, new_user)
-        mkdir(new_user_path)
-        for j in range(num_per_user):
-            index = i * new_user_num + j
-            user = user_list[index]
-            user_path = os.path.join(root_data_path, user)
-            tags = os.listdir(user_path)
-            print("------------- process the raw user: {} -------------".format(user))
-            for t in tags:
-                tag_path = os.path.join(user_path, t)
-                label_list = os.listdir(tag_path)
-                new_tag_path = os.path.join(new_user_path, t)
-                mkdir(new_tag_path)
-                for label in label_list:
-                    label_path = os.path.join(tag_path, label)
-                    img_list = os.listdir(label_path)
-                    new_label_path = os.path.join(new_tag_path, label)
-                    mkdir(new_label_path)
-
-                    for img in img_list:
-                        img_path = os.path.join(label_path, img)
-                        new_img_name = user + "_" + img
-                        new_img_path = os.path.join(new_label_path, new_img_name)
-                        shutil.copy(img_path, new_img_path)
-
-if __name__ == "__main__":
-    root_data_path = "cross_silo_femnist/femnist/3500_clients_img"
-    new_data_path = "cross_silo_femnist/femnist/35_7_client_img"
-    raw_user_num = 35
-    new_user_num = 7
-    combine_users(root_data_path, new_data_path, raw_user_num, new_user_num)
-```
-
-where `root_data_path` is the path to the original 3500 user datasets, `new_data_path` is the path to the merged dataset, `raw_user_num` specifies the total number of user datasets to be merged (needs to be <= 3500), and `new_user_num` is used to set the number of users merged by the original datasets. For example, the sample code will select the first 35 users from `cross_silo_femnist/femnist/3500_clients_img`, merge them into 7 user datasets and store them in the path `cross_silo_femnist/femnist/35_7_client_img` (the merged 7 users each contains the original 5 user dataset).
-
-The following print represents a successful merge of the datasets.
-
-```sh
-========================== combine the raw 0~4 users to the new user: dataset_0 ==========================
-------------- process the raw user: f1798_42 -------------
-------------- process the raw user: f2149_81 -------------
-------------- process the raw user: f4046_46 -------------
-------------- process the raw user: f1093_13 -------------
-------------- process the raw user: f1124_24 -------------
-========================== combine the raw 5~9 users to the new user: dataset_1 ==========================
-------------- process the raw user: f0586_11 -------------
-------------- process the raw user: f0721_31 -------------
-------------- process the raw user: f3527_33 -------------
-------------- process the raw user: f0146_33 -------------
-------------- process the raw user: f1272_09 -------------
-========================== combine the raw 10~14 users to the new user: dataset_2 ==========================
-------------- process the raw user: f0245_40 -------------
-------------- process the raw user: f2363_77 -------------
-------------- process the raw user: f3596_19 -------------
-------------- process the raw user: f2418_82 -------------
-------------- process the raw user: f2288_58 -------------
-========================== combine the raw 15~19 users to the new user: dataset_3 ==========================
-------------- process the raw user: f2249_75 -------------
-------------- process the raw user: f3681_31 -------------
-------------- process the raw user: f3766_48 -------------
-------------- process the raw user: f0537_35 -------------
-------------- process the raw user: f0614_14 -------------
-========================== combine the raw 20~24 users to the new user: dataset_4 ==========================
-------------- process the raw user: f2302_58 -------------
-------------- process the raw user: f3472_19 -------------
-------------- process the raw user: f3327_11 -------------
-------------- process the raw user: f1892_07 -------------
-------------- process the raw user: f3184_11 -------------
-========================== combine the raw 25~29 users to the new user: dataset_5 ==========================
-------------- process the raw user: f1692_18 -------------
-------------- process the raw user: f1473_30 -------------
-------------- process the raw user: f0909_04 -------------
-------------- process the raw user: f1956_19 -------------
-------------- process the raw user: f1234_26 -------------
-========================== combine the raw 30~34 users to the new user: dataset_6 ==========================
-------------- process the raw user: f0031_02 -------------
-------------- process the raw user: f0300_24 -------------
-------------- process the raw user: f4064_46 -------------
-------------- process the raw user: f2439_77 -------------
-------------- process the raw user: f1717_16 -------------
-```
-
-The following directory structure of the folder `cross_silo_femnist/femnist/35_7_client_img` is as follows:
-
-```text
-35_7_client_img  # Merge the 35 users in the FeMnist dataset into 7 client data (each containing 5 pieces of user data)
-├── dataset_0  # The dataset of Client 0
-│   ├── train   # Training dataset
-│   │   ├── 0  # Store image data corresponding to category 0
-│   │   ├── 1  # Store image data corresponding to category 1
-│   │   │        ......
-│   │   └── 61  # Store image data corresponding to category 61
-│   └── test  # Test dataset, with the same directory structure as train
-│              ......
-│
-└── dataset_6  # The dataset of Client 6
-    ├── train   # Training dataset
-    │   ├── 0  # Store image data corresponding to category 0
-    │   ├── 1  # Store image data corresponding to category 1
-    │   │        ......
-    │   └── 61  # Store image data corresponding to category 61
-    └── test  # Test dataset, with the same directory structure as train
-```
-
-## Defining the Network
-
-We choose the relatively simple LeNet network, which has seven layers without the input layer: two convolutional layers, two downsampling layers (pooling layers), and three fully connected layers. Each layer contains a different number of training parameters, as shown in the following figure:
-
-![LeNet5](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/LeNet_5.jpg)
-
-> More information about LeNet network is not described herein. For more details, please refer to .
-
-The network used for this task can be found in the script [test_cross_silo_femnist.py](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_femnist/test_cross_silo_femnist.py).
-
-For a specific understanding of the network definition process in MindSpore, please refer to [quick start](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html#building-network).
-
-## Launching the Cross-Silo Federated Task
-
-### Installing MindSpore and MindSpore Federated
-
-Both source code and downloadable distribution are included. Support CPU, GPU, Ascend hardware platforms, just choose to install according to the hardware platforms. The installation steps can be found in [MindSpore Installation Guide](https://www.mindspore.cn/install), [MindSpore Federated Installation Guide](https://www.mindspore.cn/federated/docs/en/master/federated_install.html).
-
-Currently the federated learning framework is only supported for deployment in Linux environments. Cross-silo federated learning framework requires MindSpore version number >= 1.5.0.
-
-### Launching the Task
-
-Refer to [Example](https://gitee.com/mindspore/federated/tree/master/example/cross_silo_femnist) to launch cluster. The reference example directory structure is as follows.
-
-```text
-cross_silo_femnist/
-├── config.json # Configuration file
-├── finish_cross_silo_femnist.py # Close the cross-silo federated task script
-├── run_cross_silo_femnist_sched.py # Start cross-silo federated scheduler script
-├── run_cross_silo_femnist_server.py # Start cross-silo federated server script
-├── run_cross_silo_femnist_worker.py # Start cross-silo federated worker script
-├── run_cross_silo_femnist_worker_distributed.py # Start the cloud Federation distributed training worker script
-└── test_cross_silo_femnist.py # Training scripts used by the client
-```
-
-1. Start Scheduler
-
-   `run_cross_silo_femnist_sched.py` is a Python script provided for the user to start the `Scheduler` and supports modifying the configuration via argument passing `argparse`. The following command is executed, representing the `Scheduler` that starts this federated learning task with TCP port `5554`.
-
-   ```sh
-   python run_cross_silo_femnist_sched.py --scheduler_manage_address=127.0.0.1:5554
-   ```
-
-   The following print represents a successful start-up:
-
-   ```sh
-   [INFO] FEDERATED(35566,7f4275895740,python):2022-10-09-15:23:22.450.205 [mindspore_federated/fl_arch/ccsrc/scheduler/scheduler.cc:35] Run] Scheduler started successfully.
-   [INFO] FEDERATED(35566,7f41f259d700,python):2022-10-09-15:23:22.450.357 [mindspore_federated/fl_arch/ccsrc/common/communicator/http_request_handler.cc:90] Run] Start http server!
-   ```
-
-2. Start Server
-
-   `run_cross_silo_femnist_server.py` is a Python script for the user to start a number of `Server`, and supports modify the configuration via argument passing `argparse`. The following command is executed, representing the `Server` that starts this federated learning task, with an http start port of `5555` and a number of `servers` of `4`.
-
-   ```sh
-    python run_cross_silo_femnist_server.py --local_server_num=4 --http_server_address=10.*.*.*:5555
-   ```
-
-   The above command is equivalent to starting four `Server` processes, each with a federated learning service port of `5555`, `5556`, `5557` and `5558`.
-
-3. Start Worker
-
-   `run_cross_silo_femnist_worker.py` is a Python script for the user to start a number of `worker`, and supports modify the configuration via argument passing `argparse`. The following command is executed, representing the `worker` that starts this federated learning task, with an http start port of `5555` and a number of `worker` of `4`.
-
-   ```sh
-   python run_cross_silo_femnist_worker.py --dataset_path=/data_nfs/code/fed_user_doc/federated/tests/st/cross_silo_femnist/35_7_client_img/ --http_server_address=10.*.*.*:5555
-   ```
-
-   At present, the `worker` node of the cloud federation supports the distributed training mode of single machine multi-card and multi-machine multi-card. `run_cross_silo_femnist_worker_distributed.py` is a Python script provided for users to start the distributed training of `worker` node. It also supports the configuration modification through parameter argparse. Execute the following instructions, representing the distributed `worker` that starts this federated learning task, where `device_num` represents the number of processes started by the `worker` cluster, `run_distribute` represents the distributed training started by the cluster, and its http start port is `5555`. The number of `orker` processes is `4`:
-
-   ```sh
-   python run_cross_silo_femnist_worker_distributed.py --device_num=4 --run_distribute=True --dataset_path=/data_nfs/code/fed_user_doc/federated/tests/st/cross_silo_femnist/35_7_client_img/ --http_server_address=10.*.*.*:5555
-   ```
-
-After executing the above three commands, go to the `worker_0` folder in the current directory and check the `worker_0` log with the command `grep -rn "test acc" *` and you will see a print similar to the following:
-
-```sh
-local epoch: 0, loss: 3.787421340711655, trian acc: 0.05342741935483871, test acc: 0.075
-```
-
-Then it means that cross-silo federated learning is started successfully and `worker_0` is training, other workers can be viewed in a similar way.
-
-If worker has been started in distributed multi-card training mode, enter the folder `worker_distributed/log_output/` in the current directory, and run the command `grep -rn "test acc" *` to view the log of `worker` distributed cluster. You can see the following print:
-
-```text
-local epoch: 0, loss: 2.3467453340711655, trian acc: 0.06532451988877687, test acc: 0.076
-```
-
-Please refer to [yaml configuration notes](https://www.mindspore.cn/federated/docs/zh-CN/master/horizontal/federated_server_yaml.html) for the description of parameter configuration in the above script.
-
-### Viewing Log
-
-After successfully starting the task, the corresponding log file will be generated under the current directory `cross_silo_femnist` with the following log file directory structure:
-
-```text
-cross_silo_femnist
-├── scheduler
-│   └── scheduler.log     # Print the log during running scheduler
-├── server_0
-│   └── server.log        # Print the log during running server_0
-├── server_1
-│   └── server.log        # Print the log during running server_1
-├── server_2
-│   └── server.log        # Print the log during running server_2
-├── server_3
-│   └── server.log        # Print the log during running server_3
-├── worker_0
-│   ├── ckpt              # Store the aggregated model ckpt obtained by worker_0 at the end of each federation learning iteration
-│   │   ├── 0-fl-ms-bs32-0epoch.ckpt
-│   │   ├── 0-fl-ms-bs32-1epoch.ckpt
-│   │   │
-│   │   │              ......
-│   │   │
-│   │   └── 0-fl-ms-bs32-19epoch.ckpt
-│   └── worker.log        # Record the output logs when worker_0 participates in the federated learning task
-└── worker_1
-    ├── ckpt              # Store the aggregated model ckpt obtained by worker_1 at the end of each federation learning iteration
-    │  ├── 1-fl-ms-bs32-0epoch.ckpt
-    │  ├── 1-fl-ms-bs32-1epoch.ckpt
-    │  │
-    │  │                     ......
-    │  │
-    │  └── 1-fl-ms-bs32-19epoch.ckpt
-    └── worker.log        # Record the output logs when worker_1 participates in the federated learning task
-```
-
-### Closing the Task
-
-If you want to exit in the middle, the following command is available:
-
-```sh
-python finish_cross_silo_femnist.py --redis_port=2345
-```
-
-Or wait until the training task is finished and then the cluster will exit automatically, no need to close it manually.
-
-### Results
-
-- Used data:
-
-  The `35_7_client_img/` dataset generated in the `download dataset` section above
-
-- The number of client-side local training epochs: 20
-
-- The total number of cross-silo federated learning iterations: 20
-
-- Results (accuracy of the model on the client's test set after each iteration aggregation)
-
-`worker_0` result:
-
-```sh
-worker_0/worker.log:7409:local epoch: 0, loss: 3.787421340711655, trian acc: 0.05342741935483871, test acc: 0.075
-worker_0/worker.log:14419:local epoch: 1, loss: 3.725699281115686, trian acc: 0.05342741935483871, test acc: 0.075
-worker_0/worker.log:21429:local epoch: 2, loss: 3.5285709657335795, trian acc: 0.19556451612903225, test acc: 0.16875
-worker_0/worker.log:28439:local epoch: 3, loss: 3.0393165519160608, trian acc: 0.4889112903225806, test acc: 0.4875
-worker_0/worker.log:35449:local epoch: 4, loss: 2.575952764115026, trian acc: 0.6854838709677419, test acc: 0.60625
-worker_0/worker.log:42459:local epoch: 5, loss: 2.2081101375296512, trian acc: 0.7782258064516129, test acc: 0.6875
-worker_0/worker.log:49470:local epoch: 6, loss: 1.9229739431736557, trian acc: 0.8054435483870968, test acc: 0.69375
-worker_0/worker.log:56480:local epoch: 7, loss: 1.7005576549999293, trian acc: 0.8296370967741935, test acc: 0.65625
-worker_0/worker.log:63490:local epoch: 8, loss: 1.5248727620766704, trian acc: 0.8407258064516129, test acc: 0.6375
-worker_0/worker.log:70500:local epoch: 9, loss: 1.3838803705352127, trian acc: 0.8568548387096774, test acc: 0.7
-worker_0/worker.log:77510:local epoch: 10, loss: 1.265225578921041, trian acc: 0.8679435483870968, test acc: 0.7125
-worker_0/worker.log:84520:local epoch: 11, loss: 1.167484122101638, trian acc: 0.8659274193548387, test acc: 0.70625
-worker_0/worker.log:91530:local epoch: 12, loss: 1.082880981700859, trian acc: 0.8770161290322581, test acc: 0.65625
-worker_0/worker.log:98540:local epoch: 13, loss: 1.0097520119572772, trian acc: 0.8840725806451613, test acc: 0.64375
-worker_0/worker.log:105550:local epoch: 14, loss: 0.9469810053708015, trian acc: 0.9022177419354839, test acc: 0.7
-worker_0/worker.log:112560:local epoch: 15, loss: 0.8907848935604703, trian acc: 0.9022177419354839, test acc: 0.6625
-worker_0/worker.log:119570:local epoch: 16, loss: 0.8416629644123349, trian acc: 0.9082661290322581, test acc: 0.70625
-worker_0/worker.log:126580:local epoch: 17, loss: 0.798475691030866, trian acc: 0.9122983870967742, test acc: 0.70625
-worker_0/worker.log:133591:local epoch: 18, loss: 0.7599438544427897, trian acc: 0.9243951612903226, test acc: 0.6875
-worker_0/worker.log:140599:local epoch: 19, loss: 0.7250227383907605, trian acc: 0.9294354838709677, test acc: 0.7125
-```
-
-The test results of other clients are basically the same, so the details are not listed herein.
diff --git a/docs/federated/docs/source_en/images/HFL_en.png b/docs/federated/docs/source_en/images/HFL_en.png
deleted file mode 100644
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-
-Advantages of the MindSpore Federated Horizontal Framework
------------------------------------------------------------
-
-Horizontal Federated Architecture:
-
-.. raw:: html
-
-   
-
-1. Privacy Protection
-
-   It supports accuracy-lossless security aggregation solution based on secure multi-party computation (MPC) to prevent model theft.
-
-   It supports performance-lossless encryption based on local differential privacy to prevent private data leakage from models.
-
-   It supports a gradient protection scheme based on Symbolic Dimensional Selection (SignDS), which prevents model privacy data leakage while reducing communication overhead by 99%.
-
-2. Distributed Federated Aggregation
-
-   The loosely coupled cluster processing mode on the cloud and distributed gradient quadratic aggregation paradigms support the deployment of tens of millions of heterogeneous devices, implements high-performance and high-availability federated aggregation computing, and can cope with network instability and sudden load changes.
-
-3. Federated Learning Efficiency Improvement
-
-   The adaptive frequency modulation strategy and gradient compression algorithm are supported to improve the federated learning efficiency and saving bandwidth resources.
-
-   Multiple federated aggregation policies are supported to improve the smoothness of federated learning convergence and optimize both global and local accuracies.
-
-4. Easy to Use
-
-   Only one line of code is required to switch between the standalone training and federated learning modes.
-
-   The network models, aggregation algorithms, and security algorithms are programmable, and the security level can be customized.
-
-   It supports the effectiveness evaluation of federated training models and provides monitoring capabilities for federated tasks.
-
-Advantages of the MindSpore Federated Vertical Framework
------------------------------------------------------------
-
-Vertical Federated Architecture:
-
-.. raw:: html
-
-   
-
-1. Privacy Protection
-
-   Support high-performance Privacy Set Intersection Protocol (PSI), which prevents federated participants from obtaining ID information outside the intersection and can cope with data imbalance scenarios.
-
-   Support feature protection software solution that combines quantization and differential privacy, to prevent attackers from reconstructing original privacy data from intermediate features.
-
-   Support feature protection hardware solution which is based on trusted execution environment, to provide high-strength and efficient feature protection capabilities.
-
-   Support label protection solution which is based on differential privacy, to prevent the leakage of user label data.
-
-2. Federated training
-
-   Support multiple types of split learning network structures.
-
-   Cross-domain training for large models with pipelined parallel optimization.
-
-MindSpore Federated Working Process
-------------------------------------
-
-1. `Scenario Identification and Data Accumulation `_
-
-   Identify scenarios where federated learning is used and accumulate local data for federated tasks on the client.
-
-2. `Model Selection and Framework Deployment `_
-
-   Select or develop a model prototype and use a tool to generate a federated learning model that is easy to deploy.
-
-3. `Application Deployment `_
-
-   Deploy the corresponding components to the business application and set up federated configuration tasks and deployment scripts on the server.
-
-Common Application Scenarios
-----------------------------
-
-1. `Image Classification `_
-
-   Use the federated learning to implement image classification applications.
-
-2. `Text Classification `_
-
-   Use the federated learning to implement text classification applications.
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Deployment
-
-   federated_install
-   deploy_federated_server
-   deploy_federated_client
-   deploy_vfl
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Horizontal Application
-
-   image_classfication_dataset_process
-   image_classification_application
-   sentiment_classification_application
-   image_classification_application_in_cross_silo
-   object_detection_application_in_cross_silo
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Vertical Application
-
-   data_join
-   split_wnd_application
-   split_pangu_alpha_application
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Security and Privacy
-
-   local_differential_privacy_training_noise
-   local_differential_privacy_training_signds
-   local_differential_privacy_eval_laplace
-   pairwise_encryption_training
-   private_set_intersection
-   secure_vertical_federated_learning_with_EmbeddingDP
-   secure_vertical_federated_learning_with_TEE
-   secure_vertical_federated_learning_with_DP
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Communication Compression
-
-   communication_compression
-   vfl_communication_compress
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Horizontal Federated API Reference
-
-   horizontal_server
-   cross_device
-   horizontal/cross_silo
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Vertical Federated API Reference
-
-   Data_Join
-   vertical/vertical_communicator
-   vertical_federated_trainer
-
-.. toctree::
-   :maxdepth: 1
-   :caption: References
-
-   faq
-
-.. toctree::
-   :glob:
-   :maxdepth: 1
-   :caption: RELEASE NOTES
-
-   RELEASE
diff --git a/docs/federated/docs/source_en/interface_description_federated_client.md b/docs/federated/docs/source_en/interface_description_federated_client.md
deleted file mode 100644
index abc50a417e..0000000000
--- a/docs/federated/docs/source_en/interface_description_federated_client.md
+++ /dev/null
@@ -1,350 +0,0 @@
-# Examples
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/interface_description_federated_client.md)
-
-Note that before using the following interfaces, you can first refer to the document [on-device deployment](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html) to deploy related environments.
-
-## flJobRun() for Starting Federated Learning
-
-Before calling the flJobRun() API, instantiate the parameter class FLParameter and set related parameters as follows:
-
-| Parameter            | Type                         | Mandatory | Description                                                  | Remarks                                                      |
-| -------------------- | ---------------------------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| dataMap              | Map/> | Y         | The path of Federated learning dataset.                      | The dataset of Map/> type, the key in the map is the RunType enumeration type, the value is the corresponding dataset list, when the key is RunType.TRAINMODE, the corresponding value is the training-related dataset list, when the key  is RunType.EVALMODE, it means that the corresponding value is a list of verification-related datasets, and when the key is RunType.INFERMODE, it means that the corresponding value is a list of inference-related datasets. |
-| flName               | String                       | Y         | The package path of model script used by federated learning. | We provide two types of model scripts for your reference ([Supervised sentiment classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert)), ([LeNet image classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet)). For supervised sentiment classification tasks, this parameter can be set to the package path of the provided script file [AlBertClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert/AlbertClient.java), like as `com.mindspore.flclient.demo.albert.AlbertClient`; for LeNet image classification tasks, this parameter can be set to the package path of the provided script file [LenetClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet/LenetClient.java), like as `com.mindspore.flclient.demo.lenet.LenetClient`. At the same time, users can refer to these two types of model scripts, define the model script by themselves, and then set the parameter to the package path of the customized model file ModelClient.java (which needs to inherit from the class [Client.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/model/Client.java)). |
-| trainModelPath       | String                       | Y         | Path of a training model used for federated learning, which is an absolute path of the .ms file. | It is recommended to set the path to the training App's own directory to protect the data access security of the model itself. |
-| inferModelPath       | String                       | Y         | Path of an inference model used for federated learning, which is an absolute path of the .ms file. | For the normal federated learning mode (training and inference use the same model), the value of this parameter needs to be the same as that of `trainModelPath`; for the hybrid learning mode (training and inference use different models, and the server side also includes training process), this parameter is set to the path of actual inference model. It is recommended to set the path to the training App's own directory to protect the data access security of the model itself. |
-| sslProtocol          | String                       | N         | The TLS protocol version used by the device-cloud HTTPS communication. | A whitelist is set, and currently only "TLSv1.3" or "TLSv1.2" is supported. Only need to set it up in the HTTPS communication scenario. |
-| deployEnv            | String                       | Y         | The deployment environment for federated learning.           | A whitelist is set, currently only "x86", "android" are supported. |
-| certPath             | String                       | N         | The self-signed root certificate path used for device-cloud HTTPS communication. | When the deployment environment is "x86" and the device-cloud uses a self-signed certificate for HTTPS communication authentication, this parameter needs to be set. The certificate must be consistent with the CA root certificate used to generate the cloud-side self-signed certificate to pass the verification. This parameter is used for non-Android scenarios. |
-| domainName           | String                       | Y         | The url for device-cloud communication.                      | Currently, https and http communication are supported, the corresponding formats are like: https://......, http://......, and when `useElb` is set to true, the format must be: https://127.0.0.0 : 6666 or http://127.0.0.0 : 6666 , where `127.0.0.0` corresponds to the ip of the machine providing cloud-side services (corresponding to the cloud-side parameter `--scheduler_ip`), and `6666` corresponds to the cloud-side parameter `--fl_server_port`. |
-| ifUseElb             | boolean                      | N         | Used for multi-server scenarios to set whether to randomly send client requests to different servers within a certain range. | Setting to true means that the client will randomly send requests to a certain range of server addresses, and false means that the client's requests will be sent to a fixed server address. This parameter is used in non-Android scenarios, and the default value is false. |
-| serverNum            | int                          | N         | The number of servers that the client can choose to connect to. | When `ifUseElb` is set to true, it can be set to be consistent with the `server_num` parameter when the server is started on the cloud side. It is used to randomly select different servers to send information. This parameter is used in non-Android scenarios. The default value is 1. |
-| ifPkiVerify          | boolean                      | N         | The switch of device-cloud identity authentication.          | Set to true to enable device-cloud security authentication, set to false to disable, and the default value is false. Identity authentication requires HUKS to provide a certificate. This parameter is only used in the Android environment (currently only supports HUAWEI phones). |
-| threadNum            | int                          | N         | The number of threads used in federated learning training and inference. | The default value is 1.                                      |
-| cpuBindMode          | BindMode                     | N         | The cpu core that threads need to bind during federated learning training and inference. | It is the enumeration type `BindMode`, where BindMode.NOT_BINDING_CORE represents the unbound core, which is automatically assigned by the system, BindMode.BIND_LARGE_CORE represents the bound large core, and BindMode.BIND_MIDDLE_CORE represents the bound middle core. The default value is BindMode.NOT_BINDING_CORE. |
-| batchSize            | int                          | Y         | The number of single-step training samples used in federated learning training and inference, that is, batch size. | It needs to be consistent with the batch size of the input data of the model. |
-| iflJobResultCallback | IFLJobResultCallback         | N         | The federated learning callback function object `iflJobResultCallback`. | The user can implement the specific method of the interface class [IFLJobResultCallback.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/IFLJobResultCallback.java) in the project according to the needs of the actual scene, and set it as a callback function object in the federated learning task. We provide a simple implementation use case [FLJobResultCallback.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/IFLJobResultCallback.java) as the default value of this parameter. |
-
-Note 1: When using HTTP communication, there may exist communication security risks, please be aware.
-
-Note 2: In the Android environment, the following parameters need to be set when using HTTPS communication. The setting examples are as follows:
-
-```java
-FLParameter flParameter = FLParameter.getInstance();
-SecureSSLSocketFactory sslSocketFactory = SecureSSLSocketFactory.getInstance(applicationContext)
-SecureX509TrustManager x509TrustManager = new SecureX509TrustManager(applicationContext);
-flParameter.setSslSocketFactory(sslSocketFactory);
-flParameter.setX509TrustManager(x509TrustManager);
-```
-
-Among them, the two objects `SecureSSLSocketFactory` and `SecureX509TrustManager` need to be implemented in the Android project, and users need to design by themselves according to the type of certificate in the mobile phone.
-
-Note 3: In the x86 environment, currently only self-signed certificate authentication is supported when using HTTPS communication, and the following parameters need to be set. The setting examples are as follows:
-
-```java
-FLParameter flParameter = FLParameter.getInstance();
-String certPath  =  "CARoot.pem";             //  the self-signed root certificate path used for device-cloud HTTPS communication.
-flParameter.setCertPath(certPath);
-```
-
-Note 4: In the Android environment, when `pkiVerify` is set to true and encrypt_train_type is set to PW_ENCRYPT on the cloud side, the following parameters need to be set. The setting examples are as follows:
-
-```java
-FLParameter flParameter = FLParameter.getInstance();
-String equipCrlPath = certPath;
-long validIterInterval = 3600000;
-flParameter.setEquipCrlPath(equipCrlPath);
-flParameter.setValidInterval(validIterInterval);
-```
-
-Among them, `equipCrlPath` is the CRL certificate required for certificate verification among devices, that is, the certificate revocation list. Generally, the device certificate CRL in "Huawei CBG Certificate Revocation Lists" can be preset; `validIterInterval` which is used to help prevent replay attacks in PW_ENCRYPT mode can generally be set to the time required for each round of device-cloud aggregation (unit: milliseconds, the default value is 3600000).
-
-Note 5: Before each federated learning task is started, the FLParameter class will be instantiated for related parameter settings. When FLParameter is instantiated, a clientID is automatically generated randomly, which is used to uniquely identify the client during the interaction with the cloud side. If the user needs to set the clientID by himself, after instantiating the FLParameter class, call its setClientID method to set it, and then after starting the federated learning task, the clientID set by the user will be used.
-
-Create a SyncFLJob object and use the flJobRun() method of the SyncFLJob class to start a federated learning task.
-
-The sample code (basic http communication) is as follows:
-
-1. Sample code of a supervised sentiment classification task
-
-   ```java
-   // create dataMap
-   String trainTxtPath = "data/albert/supervise/client/1.txt";
-   String evalTxtPath = "data/albert/supervise/eval/eval.txt";      // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   String vocabFile = "data/albert/supervise/vocab.txt";                // Path of the dictionary file for data preprocessing.
-   String idsFile = "data/albert/supervise/vocab_map_ids.txt"   // Path of the mapping ID file of a dictionary.
-   Map> dataMap = new HashMap<>();
-   List trainPath = new ArrayList<>();
-   trainPath.add(trainTxtPath);
-   trainPath.add(vocabFile);
-   trainPath.add(idsFile);
-   List evalPath = new ArrayList<>();    // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   evalPath.add(evalTxtPath);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   evalPath.add(vocabFile);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   evalPath.add(idsFile);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   dataMap.put(RunType.TRAINMODE, trainPath);
-   dataMap.put(RunType.EVALMODE, evalPath);      // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-
-   String flName = "com.mindspore.flclient.demo.albert.AlbertClient";                             // The package path of AlBertClient.java
-   String trainModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path
-   String inferModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path, consistent with trainModelPath
-   String sslProtocol = "TLSv1.2";
-   String deployEnv = "android";
-   String domainName = "http://10.*.*.*:6668";
-   boolean ifUseElb = true;
-   int serverNum = 4;
-   int threadNum = 4;
-   BindMode cpuBindMode = BindMode.NOT_BINDING_CORE;
-   int batchSize = 32;
-
-   FLParameter flParameter = FLParameter.getInstance();
-   flParameter.setFlName(flName);
-   flParameter.setDataMap(dataMap);
-   flParameter.setTrainModelPath(trainModelPath);
-   flParameter.setInferModelPath(inferModelPath);
-   flParameter.setSslProtocol(sslProtocol);
-   flParameter.setDeployEnv(deployEnv);
-   flParameter.setDomainName(domainName);
-   flParameter.setUseElb(useElb);
-   flParameter.setServerNum(serverNum);
-   flParameter.setThreadNum(threadNum);
-   flParameter.setCpuBindMode(BindMode.valueOf(cpuBindMode));
-
-   // start FLJob
-   SyncFLJob syncFLJob = new SyncFLJob();
-   syncFLJob.flJobRun();
-   ```
-
-2. Sample code of a LeNet image classification task
-
-   ```java
-   // create dataMap
-   String trainImagePath = "SyncFLClient/data/3500_clients_bin/f0178_39/f0178_39_bn_9_train_data.bin";
-   String trainLabelPath = "SyncFLClient/data/3500_clients_bin/f0178_39/f0178_39_bn_9_train_label.bin";
-   String evalImagePath = "SyncFLClient/data/3500_clients_bin/f0178_39/f0178_39_bn_1_test_data.bin";   // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   String evalLabelPath = "SyncFLClient/data/3500_clients_bin/f0178_39/f0178_39_bn_1_test_label.bin";   // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   Map> dataMap = new HashMap<>();
-   List trainPath = new ArrayList<>();
-   trainPath.add(trainImagePath);
-   trainPath.add(trainLabelPath);
-   List evalPath = new ArrayList<>();    // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   evalPath.add(evalImagePath);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   evalPath.add(evalLabelPath);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-   dataMap.put(RunType.TRAINMODE, trainPath);
-   dataMap.put(RunType.EVALMODE, evalPath);      // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-
-   String flName = "com.mindspore.flclient.demo.lenet.LenetClient";                         // The package path of LenetClient.java
-   String trainModelPath = "SyncFLClient/lenet_train.mindir0.ms";                      // Absolute path
-   String inferModelPath = "SyncFLClient/lenet_train.mindir0.ms";                      // Absolute path, consistent with trainModelPath
-   String sslProtocol = "TLSv1.2";
-   String deployEnv = "android";
-   String domainName = "http://10.*.*.*:6668";
-   boolean ifUseElb = true;
-   int serverNum = 4;
-   int threadNum = 4;
-   BindMode cpuBindMode = BindMode.NOT_BINDING_CORE;
-   int batchSize = 32;
-
-   FLParameter flParameter = FLParameter.getInstance();
-   flParameter.setFlName(flName);
-   flParameter.setDataMap(dataMap);
-   flParameter.setTrainModelPath(trainModelPath);
-   flParameter.setInferModelPath(inferModelPath);
-   flParameter.setSslProtocol(sslProtocol);
-   flParameter.setDeployEnv(deployEnv);
-   flParameter.setDomainName(domainName);
-   flParameter.setUseElb(useElb);
-   flParameter.setServerNum(serverNum);
-   flParameter.setThreadNum(threadNum);
-   flParameter.setCpuBindMode(BindMode.valueOf(cpuBindMode));
-   flParameter.setBatchSize(batchSize);
-
-   // start FLJob
-   SyncFLJob syncFLJob = new SyncFLJob();
-   syncFLJob.flJobRun();
-   ```
-
-## modelInference() for Inferring Multiple Input Data Records
-
-Before calling the modelInference() API, instantiate the parameter class FLParameter and set related parameters as follows:
-
-| Parameter      | Type                         | Mandatory | Description                                                  | Remarks                                                      |
-| -------------- | ---------------------------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| flName         | String                       | Y         | The package path of model script used by federated learning. | We provide two types of model scripts for your reference ([Supervised sentiment classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert), [LeNet image classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet)). For supervised sentiment classification tasks, this parameter can be set to the package path of the provided script file [AlBertClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert/AlbertClient.java), like as `com.mindspore.flclient.demo.albert.AlbertClient`; for LeNet image classification tasks, this parameter can be set to the package path of the provided script file [LenetClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet/LenetClient.java), like as `com.mindspore.flclient.demo.lenet.LenetClient`. At the same time, users can refer to these two types of model scripts, define the model script by themselves, and then set the parameter to the package path of the customized model file ModelClient.java (which needs to inherit from the class [Client.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/model/Client.java)). |
-| dataMap        | Map/> | Y         | The path of Federated learning dataset.                      | The dataset of Map/> type, the key in the map is the RunType enumeration type, the value is the corresponding dataset list, when the key is RunType.TRAINMODE, the corresponding value is the training-related dataset list, when the key  is RunType.EVALMODE, it means that the corresponding value is a list of verification-related datasets, and when the key is RunType.INFERMODE, it means that the corresponding value is a list of inference-related datasets. |
-| inferModelPath | String                       | Y         | Path of an inference model used for federated learning, which is an absolute path of the .ms file. | For the normal federated learning mode (training and inference use the same model), the value of this parameter needs to be the same as that of `trainModelPath`; for the hybrid learning mode (training and inference use different models, and the server side also includes training process), this parameter is set to the path of actual inference model. It is recommended to set the path to the training App's own directory to protect the data access security of the model itself. |
-| threadNum      | int                          | N         | The number of threads used in federated learning training and inference. | The default value is 1.                                      |
-| cpuBindMode    | BindMode                     | N         | The cpu core that threads need to bind during federated learning training and inference. | It is the enumeration type `BindMode`, where BindMode.NOT_BINDING_CORE represents the unbound core, which is automatically assigned by the system, BindMode.BIND_LARGE_CORE represents the bound large core, and BindMode.BIND_MIDDLE_CORE represents the bound middle core. The default value is BindMode.NOT_BINDING_CORE. |
-| batchSize      | int                          | Y         | The number of single-step training samples used in federated learning training and inference, that is, batch size. | It needs to be consistent with the batch size of the input data of the model. |
-
-Create a SyncFLJob object and use the modelInference() method of the SyncFLJob class to start an inference task on the device. The inferred label array is returned.
-
-The sample code is as follows:
-
-1. Sample code of a supervised sentiment classification task
-
-   ```java
-   // create dataMap
-   String inferTxtPath = "data/albert/supervise/eval/eval.txt";
-   String vocabFile = "data/albert/supervise/vocab.txt";
-   String idsFile = "data/albert/supervise/vocab_map_ids.txt"
-   Map> dataMap = new HashMap<>();
-   List inferPath = new ArrayList<>();
-   inferPath.add(inferTxtPath);
-   inferPath.add(vocabFile);
-   inferPath.add(idsFile);
-   dataMap.put(RunType.INFERMODE, inferPath);
-
-   String flName = "com.mindspore.flclient.demo.albert.AlbertClient";                             // The package path of AlBertClient.java
-   String inferModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path, consistent with trainModelPath
-   int threadNum = 4;
-   BindMode cpuBindMode = BindMode.NOT_BINDING_CORE;
-   int batchSize = 32;
-
-   FLParameter flParameter = FLParameter.getInstance();
-   flParameter.setFlName(flName);
-   flParameter.setDataMap(dataMap);
-   flParameter.setInferModelPath(inferModelPath);
-   flParameter.setThreadNum(threadNum);
-   flParameter.setCpuBindMode(BindMode.valueOf(cpuBindMode));
-   flParameter.setBatchSize(batchSize);
-
-   // inference
-   SyncFLJob syncFLJob = new SyncFLJob();
-   int[] labels = syncFLJob.modelInference();
-   ```
-
-2. Sample code of a LeNet image classification
-
-   ```java
-   // create dataMap
-   String inferImagePath = "SyncFLClient/data/3500_clients_bin/f0178_39/f0178_39_bn_1_test_data.bin";
-   String inferLabelPath = "SyncFLClient/data/3500_clients_bin/f0178_39/f0178_39_bn_1_test_label.bin";
-   Map> dataMap = new HashMap<>();
-   List inferPath = new ArrayList<>();
-   inferPath.add(inferImagePath);
-   inferPath.add(inferLabelPath);
-   dataMap.put(RunType.INFERMODE, inferPath);
-
-   String flName = "com.mindspore.flclient.demo.lenet.LenetClient";                         // The package path of LenetClient.java package
-   String inferModelPath = "SyncFLClient/lenet_train.mindir0.ms";                              // Absolute path, consistent with trainModelPath
-   int threadNum = 4;
-   BindMode cpuBindMode = BindMode.NOT_BINDING_CORE;
-   int batchSize = 32;
-
-   FLParameter flParameter = FLParameter.getInstance();
-   flParameter.setFlName(flName);
-   flParameter.setDataMap(dataMap);
-   flParameter.setInferModelPath(inferModelPath);
-   flParameter.setThreadNum(threadNum);
-   flParameter.setCpuBindMode(BindMode.valueOf(cpuBindMode));
-   flParameter.setBatchSize(batchSize);
-
-   // inference
-   SyncFLJob syncFLJob = new SyncFLJob();
-   int[] labels = syncFLJob.modelInference();
-   ```
-
-## getModel() for Obtaining the Latest Model on the Cloud
-
-Before calling the getModel() API, instantiate the parameter class FLParameter and set related parameters as follows:
-
-| Parameter      | Type      | Mandatory | Description                                                  | Remarks                                                      |
-| -------------- | --------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| flName         | String    | Y         | The package path of model script used by federated learning. | We provide two types of model scripts for your reference ([Supervised sentiment classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert), [LeNet image classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet)). For supervised sentiment classification tasks, this parameter can be set to the package path of the provided script file [AlBertClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert/AlbertClient.java), like as `com.mindspore.flclient.demo.albert.AlbertClient`; for LeNet image classification tasks, this parameter can be set to the package path of the provided script file [LenetClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet/LenetClient.java), like as `com.mindspore.flclient.demo.lenet.LenetClient`. At the same time, users can refer to these two types of model scripts, define the model script by themselves, and then set the parameter to the package path of the customized model file ModelClient.java (which needs to inherit from the class [Client.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/model/Client.java)). |
-| trainModelPath | String    | Y         | Path of a training model used for federated learning, which is an absolute path of the .ms file. | It is recommended to set the path to the training App's own directory to protect the data access security of the model itself. |
-| inferModelPath | String    | Y         | Path of an inference model used for federated learning, which is an absolute path of the .ms file. | For the normal federated learning mode (training and inference use the same model), the value of this parameter needs to be the same as that of `trainModelPath`; for the hybrid learning mode (training and inference use different models, and the server side also includes training process), this parameter is set to the path of actual inference model. It is recommended to set the path to the training App's own directory to protect the data access security of the model itself. |
-| sslProtocol    | String    | N         | The TLS protocol version used by the device-cloud HTTPS communication. | A whitelist is set, and currently only "TLSv1.3" or "TLSv1.2" is supported. Only need to set it up in the HTTPS communication scenario. |
-| deployEnv      | String    | Y         | The deployment environment for federated learning.           | A whitelist is set, currently only "x86", "android" are supported. |
-| certPath       | String    | N         | The self-signed root certificate path used for device-cloud HTTPS communication. | When the deployment environment is "x86" and the device-cloud uses a self-signed certificate for HTTPS communication authentication, this parameter needs to be set. The certificate must be consistent with the CA root certificate used to generate the cloud-side self-signed certificate to pass the verification. This parameter is used for non-Android scenarios. |
-| domainName     | String    | Y         | The url for device-cloud communication.                      | Currently, https and http communication are supported, the corresponding formats are like: https://......, http://......, and when `useElb` is set to true, the format must be: https://127.0.0.0 : 6666 or http://127.0.0.0 : 6666 , where `127.0.0.0` corresponds to the ip of the machine providing cloud-side services (corresponding to the cloud-side parameter `--scheduler_ip`), and `6666` corresponds to the cloud-side parameter `--fl_server_port`. |
-| ifUseElb       | boolean   | N         | Used for multi-server scenarios to set whether to randomly send client requests to different servers within a certain range. | Setting to true means that the client will randomly send requests to a certain range of server addresses, and false means that the client's requests will be sent to a fixed server address. This parameter is used in non-Android scenarios, and the default value is false. |
-| serverNum      | int       | N         | The number of servers that the client can choose to connect to. | When `ifUseElb` is set to true, it can be set to be consistent with the `server_num` parameter when the server is started on the cloud side. It is used to randomly select different servers to send information. This parameter is used in non-Android scenarios. The default value is 1. |
-| serverMod      | ServerMod | Y         | The federated learning training mode.                        | The federated learning training mode of ServerMod enumeration type, where ServerMod.FEDERATED_LEARNING represents the normal federated learning mode (training and inference use the same model) ServerMod.HYBRID_TRAINING represents the hybrid learning mode (training and inference use different models, and the server side also includes training process). |
-
-Note 1: When using HTTP communication, there may exist communication security risks, please be aware.
-
-Note 2: In the Android environment, the following parameters need to be set when using HTTPS communication. The setting examples are as follows:
-
-```java
-FLParameter flParameter = FLParameter.getInstance();
-SecureSSLSocketFactory sslSocketFactory = SecureSSLSocketFactory.getInstance(applicationContext)
-SecureX509TrustManager x509TrustManager = new SecureX509TrustManager(applicationContext);
-flParameter.setSslSocketFactory(sslSocketFactory);
-flParameter.setX509TrustManager(x509TrustManager);
-```
-
-Among them, the two objects `SecureSSLSocketFactory` and `SecureX509TrustManager` need to be implemented in the Android project, and users need to design themselves according to the type of certificate in the mobile phone.
-
-Note 3: In the x86 environment, currently only self-signed certificate authentication is  supported when using HTTPS communication, and the following parameters need to be set. The setting examples are as follows:
-
-```java
-FLParameter flParameter = FLParameter.getInstance();
-String certPath  =  "CARoot.pem";             //  the self-signed root certificate path used for device-cloud HTTPS communication.
-flParameter.setCertPath(certPath);
-```
-
-Note 4: Before calling the getModel method, the FLParameter class will be instantiated for related parameter settings. When FLParameter is instantiated, a clientID is automatically generated randomly, which is used to uniquely identify the client during the interaction with the cloud side. If the user needs to set the clientID by himself, after instantiating the FLParameter class, call its setCertPath method to set it, and then after starting the getModel task, the clientID set by the user will be used.
-
-Create a SyncFLJob object and use the getModel() method of the SyncFLJob class to start an asynchronous inference task. The status code of the getModel request is returned.
-
-The sample code is as follows:
-
-1. Supervised sentiment classification task
-
-   ```java
-   String flName = "com.mindspore.flclient.demo.albert.AlbertClient";                             // The package path of AlBertClient.java package
-   String trainModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path
-   String inferModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path, consistent with trainModelPath
-   String sslProtocol = "TLSv1.2";
-   String deployEnv = "android";
-   String domainName = "http://10.*.*.*:6668";
-   boolean ifUseElb = true;
-   int serverNum = 4;
-   ServerMod serverMod = ServerMod.FEDERATED_LEARNING;
-
-   FLParameter flParameter = FLParameter.getInstance();
-   flParameter.setFlName(flName);
-   flParameter.setTrainModelPath(trainModelPath);
-   flParameter.setInferModelPath(inferModelPath);
-   flParameter.setSslProtocol(sslProtocol);
-   flParameter.setDeployEnv(deployEnv);
-   flParameter.setDomainName(domainName);
-   flParameter.setUseElb(useElb);
-   flParameter.setServerNum(serverNum);
-   flParameter.setServerMod(ServerMod.valueOf(serverMod));
-
-   // getModel
-   SyncFLJob syncFLJob = new SyncFLJob();
-   syncFLJob.getModel();
-   ```
-
-2. LeNet image classification task
-
-   ```java
-   String flName = "com.mindspore.flclient.demo.lenet.LenetClient";                         // The package path of LenetClient.java package
-   String trainModelPath = "SyncFLClient/lenet_train.mindir0.ms";                             // Absolute path
-   String inferModelPath = "SyncFLClient/lenet_train.mindir0.ms";                             // Absolute path, consistent with trainModelPath
-   String sslProtocol = "TLSv1.2";
-   String deployEnv = "android";
-   String domainName = "http://10.*.*.*:6668";
-   boolean ifUseElb = true;
-   int serverNum = 4
-   ServerMod serverMod = ServerMod.FEDERATED_LEARNING;
-
-   FLParameter flParameter = FLParameter.getInstance();
-   flParameter.setFlName(flName);
-   flParameter.setTrainModelPath(trainModelPath);
-   flParameter.setInferModelPath(inferModelPath);
-   flParameter.setSslProtocol(sslProtocol);
-   flParameter.setDeployEnv(deployEnv);
-   flParameter.setDomainName(domainName);
-   flParameter.setUseElb(useElb);
-   flParameter.setServerNum(serverNum);
-   flParameter.setServerMod(ServerMod.valueOf(serverMod));
-
-   // getModel
-   SyncFLJob syncFLJob = new SyncFLJob();
-   syncFLJob.getModel();
-   ```
diff --git a/docs/federated/docs/source_en/java_api_callback.md b/docs/federated/docs/source_en/java_api_callback.md
deleted file mode 100644
index b1fc6d1a83..0000000000
--- a/docs/federated/docs/source_en/java_api_callback.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# Callback
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/java_api_callback.md)
-
-```java
-import com.mindspore.flclient.model.Callback
-```
-
-Callback defines the hook function used to record training, evaluate and predict the results of different stages in end-to-side federated learning.
-
-## Public Member Functions
-
-| function                    |
-| -------------------------------- |
-| [abstract Status stepBegin()](#stepbegin) |
-| [abstract Status stepEnd()](#stepend)   |
-| [abstract Status epochBegin()](#epochbegin) |
-| [abstract Status epochEnd()](#epochend) |
-
-## stepBegin
-
-```java
-   public abstract Status stepBegin()
-```
-
-Execute step begin function.
-
-- Returns
-
-  Whether the execution is successful.
-
-## stepEnd
-
-```java
-public abstract Status stepEnd()
-```
-
-Execute step end function.
-
-- Returns
-
-  Whether the execution is successful.
-
-## epochBegin
-
-```java
-public abstract Status epochBegin()
-```
-
-Execute epoch begin function.
-
-- Returns
-
-  Whether the execution is successful.
-
-## epochEnd
-
-```java
-public abstract Status epochEnd()
-```
-
-Execute epoch end function.
-
-- Returns
-
-  Whether the execution is successful.
diff --git a/docs/federated/docs/source_en/java_api_client.md b/docs/federated/docs/source_en/java_api_client.md
deleted file mode 100644
index 92816018ea..0000000000
--- a/docs/federated/docs/source_en/java_api_client.md
+++ /dev/null
@@ -1,173 +0,0 @@
-# Client
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/java_api_client.md)
-
-```java
-import com.mindspore.flclient.model.Client
-```
-
-Client defines the execution process object of the end-side federated learning algorithm.
-
-## Public Member Functions
-
-| function                   |
-| -------------------------------- |
-| [abstract List initCallbacks(RunType runType, DataSet dataSet)](#initcallbacks) |
-| [abstract Map initDataSets(Map\> files)](#initdatasets)    |
-| [abstract float getEvalAccuracy(List evalCallbacks)](#getevalaccuracy) |
-| [abstract List getInferResult(List inferCallbacks)](#getinferresult) |
-| [Status trainModel(int epochs)](#trainmodel) |
-| [float evalModel()](#evalmodel) |
-| [Map genUnsupervisedEvalData(List evalCallbacks)](#genunsupervisedevaldata) |
-| [List inferModel()](#infermodel) |
-| [Status setLearningRate(float lr)](#setlearningrate) |
-| [void setBatchSize(int batchSize)](#setbatchsize) |
-
-## initCallbacks
-
-```java
-public abstract List initCallbacks(RunType runType, DataSet dataSet)
-```
-
-Initialize the callback list.
-
-- Parameters
-
-    - `runType`: RunType class, identify whether the training, evaluation or prediction phase.
-    - `dataSet`: DataSet class, identify whether the training, evaluation or prediction phase datasets.
-
-- Returns
-
-  The initialized callback list.
-
-## initDataSets
-
-```java
-public abstract Map initDataSets(Map> files)
-```
-
-Initialize dataset list.
-
-- Parameters
-
-    - `files`: Data files used in the training, evaluation or prediction phase.
-
-- Returns
-
-  Data counts in different run type.
-
-## getEvalAccuracy
-
-```java
-public abstract float getEvalAccuracy(List evalCallbacks)
-```
-
-Get eval model accuracy.
-
-- Parameters
-
-    - `evalCallbacks`: Callback used in eval phase.
-
-- Returns
-
-   The accuracy in eval phase.
-
-## getInferResult
-
-```java
-public abstract List getInferResult(List inferCallbacks)
-```
-
-Get infer phase result.
-
-- Parameters
-
-    - `inferCallbacks`: Callback used in prediction phase.
-
-- Returns
-
-  predict results.
-
-## trainModel
-
-```java
-public Status trainModel(int epochs)
-```
-
-Execute train model process.
-
-- Parameters
-
-    - `epochs`: Epoch num used in train process.
-
-- Returns
-
-  Whether the train model is successful.
-
-## evalModel
-
-```java
-public float evalModel()
-```
-
-Execute eval model process.
-
-- Returns
-
-  The accuracy in eval process.
-
-## genUnsupervisedEvalData
-
-```java
-public Map genUnsupervisedEvalData(List evalCallbacks)
-```
-
-Generate unsupervised training evaluation data, and the subclass needs to rewrite this function.
-
-- Parameters
-
-    - `evalCallbacks`: the eval Callback that generates data.
-
-- Returns
-
-  unsupervised training evaluation data
-
-## inferModel
-
-```java
-public List inferModel()
-```
-
-Execute model prediction process.
-
-- Returns
-
-  The prediction result.
-
-## setLearningRate
-
-```java
-public Status setLearningRate(float lr)
-```
-
-Set learning rate.
-
-- Parameters
-
-    - `lr`: Learning rate.
-
-- Returns
-
-  Whether the set is successful.
-
-## setBatchSize
-
-```java
-public void setBatchSize(int batchSize)
-```
-
-Set batch size.
-
-- Parameters
-
-    - `batchSize`: batch size.
diff --git a/docs/federated/docs/source_en/java_api_clientmanager.md b/docs/federated/docs/source_en/java_api_clientmanager.md
deleted file mode 100644
index 03d7ae2f51..0000000000
--- a/docs/federated/docs/source_en/java_api_clientmanager.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# ClientManager
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/java_api_clientmanager.md)
-
-```java
-import com.mindspore.flclient.model.ClientManager
-```
-
-ClientManager defines end-side federated learning custom algorithm model management objects.
-
-## Public Member Functions
-
-| function                    |
-| -------------------------------- |
-| [static void registerClient(Client client)](#registerclient)  |
-| [static Client getClient(String name)](#getclient)  |
-
-## registerClient
-
-```java
-public static void registerClient(Client client)
-```
-
-Register client object.
-
-- Parameters
-
-    - `client`: Need register client object.
-
-## getClient
-
-```java
-public static Client getClient(String name)
-```
-
-Get client object.
-
-- Parameters
-
-    - `name`: Client object name.
-
-- Returns
-
-  Client object.
diff --git a/docs/federated/docs/source_en/java_api_dataset.md b/docs/federated/docs/source_en/java_api_dataset.md
deleted file mode 100644
index 990bcbfff1..0000000000
--- a/docs/federated/docs/source_en/java_api_dataset.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# DataSet
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/java_api_dataset.md)
-
-```java
-import com.mindspore.flclient.model.DataSet
-```
-
-DataSet defines end-side federated learning dataset object.
-
-## Public Member Functions
-
-| function                    |
-| -------------------------------- |
-| [abstract void fillInputBuffer(List var1, int var2)](#fillinputbuffer) |
-| [abstract void shuffle()](#shuffle)    |
-| [abstract void padding()](#padding) |
-| [abstract Status dataPreprocess(List var1)](#datapreprocess) |
-
-## fillInputBuffer
-
-```java
-public abstract void fillInputBuffer(List var1, int var2)
-```
-
-Fill input buffer data.
-
-- Parameters
-
-    - `var1`: Need fill buffer.
-    - `var2`: Need fill batch index.
-
-## shuffle
-
-```java
- public abstract void shuffle()
-```
-
-Shuffle data.
-
-## padding
-
-```java
- public abstract void padding()
-```
-
-Pad data.
-
-## dataPreprocess
-
-```java
-public abstract Status dataPreprocess(List var1)
-```
-
-Data preprocess.
-
-- Parameters
-
-    - `var1`: Data files.
-
-- Returns
-
-  Whether the execution is successful.
diff --git a/docs/federated/docs/source_en/java_api_flparameter.md b/docs/federated/docs/source_en/java_api_flparameter.md
deleted file mode 100644
index fd98074127..0000000000
--- a/docs/federated/docs/source_en/java_api_flparameter.md
+++ /dev/null
@@ -1,636 +0,0 @@
-# FLParameter
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/java_api_flparameter.md)
-
-```java
-import com.mindspore.flclient.FLParameter
-```
-
-FLParameter is used to define parameters related to federated learning.
-
-## Public Member Functions
-
-| **function**                                                 |
-| ------------------------------------------------------------ |
-| public static synchronized FLParameter getInstance()         |
-| public String getDeployEnv()                                 |
-| public void setDeployEnv(String env)                         |
-| public String getDomainName()                                |
-| public void setDomainName(String domainName)                 |
-| public String getClientID()                                  |
-| public void setClientID(String clientID)                     |
-| public String getCertPath()                                  |
-| public void setCertPath(String certPath)                     |
-| public SSLSocketFactory getSslSocketFactory()                |
-| public void setSslSocketFactory(SSLSocketFactory sslSocketFactory) |
-| public X509TrustManager getX509TrustManager()                 |
-| public void setX509TrustManager(X509TrustManager x509TrustManager) |
-| public IFLJobResultCallback getIflJobResultCallback()        |
-| public void setIflJobResultCallback(IFLJobResultCallback iflJobResultCallback) |
-| public String getFlName()                                    |
-| public void setFlName(String flName)                         |
-| public String getTrainModelPath()                            |
-| public void setTrainModelPath(String trainModelPath)         |
-| public String getInferModelPath()                            |
-| public void setInferModelPath(String inferModelPath)         |
-| public String getSslProtocol()                               |
-| public void setSslProtocol(String sslProtocol)               |
-| public int getTimeOut()                                      |
-| public void setTimeOut(int timeOut)                          |
-| public int getSleepTime()                                    |
-| public void setSleepTime(int sleepTime)                      |
-| public boolean isUseElb()                                    |
-| public void setUseElb(boolean useElb)                        |
-| public int getServerNum()                                    |
-| public void setServerNum(int serverNum)                      |
-| public boolean isPkiVerify()                                 |
-| public void setPkiVerify(boolean ifPkiVerify)                |
-| public String getEquipCrlPath()                              |
-| public void setEquipCrlPath(String certPath)                 |
-| public long getValidInterval()                               |
-| public void setValidInterval(long validInterval)             |
-| public int getThreadNum()                                    |
-| public void setThreadNum(int threadNum)                      |
-| public int getCpuBindMode()                                   |
-| public void setCpuBindMode(BindMode cpuBindMode)             |
-| public List getHybridWeightName(RunType runType)    |
-| public void setHybridWeightName(List hybridWeightName, RunType runType) |
-| public Map> getDataMap()               |
-| public void setDataMap(Map/> dataMap) |
-| public ServerMod getServerMod()                              |
-| public void setServerMod(ServerMod serverMod)                |
-| public int getBatchSize()                                    |
-| public void setBatchSize(int batchSize)                      |
-
-## getInstance
-
-```java
-public static synchronized FLParameter getInstance()
-```
-
-Obtains a single FLParameter instance.
-
-- Return value
-
-    Single object of the FLParameter type.
-
-## getDeployEnv
-
-```java
-public String getDeployEnv()
-```
-
-Obtains the deployment environment for federated learning set by users.
-
-- Return value
-
-    The deployment environment for federated learning of the string type.
-
-## setDeployEnv
-
-```java
-public void setDeployEnv(String env)
-```
-
-Used to set the deployment environment for federated learning, a whitelist is set, currently only "x86", "android" are supported.
-
-- Parameter
-
-    - `env`: the deployment environment for federated learning.
-
-## getDomainName
-
-```java
-public String getDomainName()
-```
-
-Obtains the domain name set by a user.
-
-- Return value
-
-    Domain name of the string type.
-
-## setDomainName
-
-```java
-public void setDomainName(String domainName)
-```
-
-Used to set the url for device-cloud communication. Currently, https and http communication are supported, the corresponding formats are like: https://......, http://......, and when `useElb` is set to true, the format must be: https://127.0.0.0 : 6666 or http://127.0.0.0 : 6666 , where `127.0.0.0` corresponds to the ip of the machine providing cloud-side services (corresponding to the cloud-side parameter `--scheduler_ip`), and `6666` corresponds to the cloud-side parameter `--fl_server_port`.
-
-- Parameter
-
-    - `domainName`: domain name.
-
-## getClientID
-
-```java
-public String getClientID()
-```
-
-A clientID that uniquely identifies the client is automatically generated before each federated learning task is started (if the user needs to set the clientID, setClientID is used to set before the federated learning training task is started), and this method is used to obtain the ID, which can be used to generate the relevant certificates in the device-cloud security authentication scenarios.
-
-- Return value
-
-    Unique ID of the client, which is of the string type.
-
-## setClientID
-
-```java
-public void setClientID(String clientID)
-```
-
-Used to set the clientID that uniquely identifies the client.
-
-- Parameter
-
-    - `clientID`: unique ID of the client.
-
-## getCertPath
-
-```java
-public String getCertPath()
-```
-
-Obtains the self-signed root certificate path used for device-cloud HTTPS communication.
-
-- Return value
-
-    The self-signed root certificate path of the string type.
-
-## setCertPath
-
-```java
-public void setCertPath(String certPath)
-```
-
-Sets the self-signed root certificate path used for device-cloud HTTPS communication. When the deployment environment is "x86" and the device-cloud uses a self-signed certificate for HTTPS communication authentication, this parameter needs to be set. The certificate must be consistent with the CA root certificate used to generate the cloud-side self-signed certificate to pass the verification. This parameter is used for non-Android scenarios.
-
-- Parameter
-    - `certPath`: the self-signed root certificate path used for device-cloud HTTPS communication.
-
-## getSslSocketFactory
-
-```java
-public SSLSocketFactory getSslSocketFactory()
-```
-
-Obtains the ssl certificate authentication library `sslSocketFactory` set by the user.
-
-- Return value
-
-    The ssl certificate authentication library `sslSocketFactory` , which is of the SSLSocketFactory type.
-
-## setSslSocketFactory
-
-```java
-public void setSslSocketFactory(SSLSocketFactory sslSocketFactory)
-```
-
-Used to set the ssl certificate authentication library `sslSocketFactory`.
-
-- Parameter
-    - `sslSocketFactory`: the ssl certificate authentication library.
-
-## getX509TrustManager
-
-```java
-public X509TrustManager getX509TrustManager()
-```
-
-Obtains the ssl certificate authentication manager `x509TrustManager` set by the user.
-
-- Return value
-
-    the ssl certificate authentication manager `x509TrustManager`, which is of the X509TrustManager type.
-
-## setX509TrustManager
-
-```java
-public void setX509TrustManager(X509TrustManager x509TrustManager)
-```
-
-Used to set the ssl certificate authentication manager `x509TrustManager`.
-
-- Parameter
-    - `x509TrustManager`: the ssl certificate authentication manager.
-
-## getIflJobResultCallback
-
-```java
-public IFLJobResultCallback getIflJobResultCallback()
-```
-
-Obtains the federated learning callback function object `iflJobResultCallback` set by the user.
-
-- Return value
-
-    The federated learning callback function object `iflJobResultCallback`, which is of the IFLJobResultCallback type.
-
-## setIflJobResultCallback
-
-```java
-public void setIflJobResultCallback(IFLJobResultCallback iflJobResultCallback)
-```
-
-Used to set the federated learning callback function object `iflJobResultCallback`, the user can implement the specific method of the interface class [IFLJobResultCallback.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/IFLJobResultCallback.java) in the project according to the needs of the actual scene, and set it as a callback function object in the federated learning task.
-
-- Parameter
-    - `iflJobResultCallback`: the federated learning callback function object.
-
-## getFlName
-
-```java
-public String getFlName()
-```
-
-Obtains the package path of model script set by a user.
-
-- Return value
-
-    Name of the package path of model script of the string type.
-
-## setFlName
-
-```java
-public void setFlName(String flName)
-```
-
-Sets the package path of model script . We provide two types of model scripts for your reference ([Supervised sentiment classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert), [Lenet image classification task](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet)). For supervised sentiment classification tasks, this parameter can be set to the package path of the provided script file [AlBertClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert/AlbertClient.java), like as `com.mindspore.flclient.demo.albert.AlbertClient`; for Lenet image classification tasks, this parameter can be set to the package path of the provided script file [LenetClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet/LenetClient.java), like as `com.mindspore.flclient.demo.lenet.LenetClient`. At the same time, users can refer to these two types of model scripts, define the model script by themselves, and then set the parameter to the package path of the customized model file ModelClient.java (which needs to inherit from the class [Client.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/model/Client.java)).
-
-- Parameter
-    - `flName`: package path of model script.
-
-## getTrainModelPath
-
-```java
-public String getTrainModelPath()
-```
-
-Obtains the path of the training model set by a user.
-
-- Return value
-
-    Path of the training model of the string type.
-
-## setTrainModelPath
-
-```java
-public void setTrainModelPath(String trainModelPath)
-```
-
-Sets the path of the training model.
-
-- Parameter
-    - `trainModelPath`: training model path.
-
-## getInferModelPath
-
-```java
-public String getInferModelPath()
-```
-
-Obtains the path of the inference model set by a user.
-
-- Return value
-
-    Path of the inference model of the string type.
-
-## setInferModelPath
-
-```java
-public void setInferModelPath(String inferModelPath)
-```
-
-Sets the path of the inference model.
-
-- Parameter
-    - `inferModelPath`: path of the inference model.
-
-## getSslProtocol
-
-```java
-public String getSslProtocol()
-```
-
-Obtains the TLS protocol version used by the device-cloud HTTPS communication.
-
-- Return value
-
-    The TLS protocol version used by the device-cloud HTTPS communication of the string type.
-
-## setSslProtocol
-
-```java
-public void setSslProtocol(String sslProtocol)
-```
-
-Used to set the TLS protocol version used by the device-cloud HTTPS communication, a whitelist is set, and currently only "TLSv1.3" or "TLSv1.2" is supported. Only need to set it up in the HTTPS communication scenario.
-
-- Parameter
-    - `sslProtocol`: the TLS protocol version used by the device-cloud HTTPS communication.
-
-## getTimeOut
-
-```java
-public int getTimeOut()
-```
-
-Obtains the timeout interval set by a user for device-side communication.
-
-- Return value
-
-    Timeout interval for communication on the device, which is an integer.
-
-## setTimeOut
-
-```java
-public void setTimeOut(int timeOut)
-```
-
-Sets the timeout interval for communication on the device.
-
-- Parameter
-    - `timeOut`: timeout interval for communication on the device.
-
-## getSleepTime
-
-```java
-public int getSleepTime()
-```
-
-Obtains the waiting time of repeated requests set by a user.
-
-- Return value
-
-    Waiting time of repeated requests, which is an integer.
-
-## setSleepTime
-
-```java
-public void setSleepTime(int sleepTime)
-```
-
-Sets the waiting time of repeated requests.
-
-- Parameter
-    - `sleepTime`: waiting time for repeated requests.
-
-## isUseElb
-
-```java
-public boolean isUseElb()
-```
-
-Determines whether the elastic load balancing is simulated, that is, whether a client randomly sends requests to a server address within a specified range.
-
-- Return value
-
-    The value is of the boolean type. The value true indicates that the client sends requests to a random server address within a specified range. The value false indicates that the client sends a request to a fixed server address.
-
-## setUseElb
-
-```java
-public void setUseElb(boolean useElb)
-```
-
-Determines whether to simulate the elastic load balancing, that is, whether a client randomly sends a request to a server address within a specified range.
-
-- Parameter
-    - `useElb`: determines whether to simulate the elastic load balancing. The default value is false.
-
-## getServerNum
-
-```java
-public int getServerNum()
-```
-
-Obtains the number of servers that can send requests when simulating the elastic load balancing.
-
-- Return value
-
-    Number of servers that can send requests during elastic load balancing simulation, which is an integer.
-
-## setServerNum
-
-```java
-public void setServerNum(int serverNum)
-```
-
-Sets the number of servers that can send requests during elastic load balancing simulation.
-
-- Parameter
-    - `serverNum`: number of servers that can send requests during elastic load balancing simulation. The default value is 1.
-
-## isPkiVerify
-
-```java
-public boolean isPkiVerify()
-```
-
-Whether to perform device-cloud security authentication.
-
-- Return value
-
-    The value is of the boolean type. The value true indicates that device-cloud security authentication is performed, and the value false indicates that device-cloud security authentication is not performed.
-
-## setPkiVerify
-
-```java
-public void setPkiVerify(boolean pkiVerify)
-```
-
-Determines whether to perform device-cloud security authentication.
-
-- Parameter
-
-    - `pkiVerify`: whether to perform device-cloud security authentication.
-
-## getEquipCrlPath
-
-```java
-public String getEquipCrlPath()
-```
-
-Obtains the CRL certification path `equipCrlPath` of the device certificate set by the user. This parameter is used in the Android environment.
-
-- Return value
-
-    The certification path of the string type.
-
-## setEquipCrlPath
-
-```java
-public void setEquipCrlPath(String certPath)
-```
-
-Used to set the CRL certification path of the device certificate. It is used to verify whether the digital certificate is revoked. This parameter is used in the Android environment.
-
-- Parameter
-    - `certPath`: the certification path.
-
-## getValidInterval
-
-```java
-public long getValidInterval()
-```
-
-Obtains the valid iteration interval validIterInterval set by the user. This parameter is used in the Android environment.
-
-- Return value
-
-    The valid iteration interval validIterInterval of the long type.
-
-## setValidInterval
-
-```java
-public void setValidInterval(long validInterval)
-```
-
-Used to set the valid iteration interval validIterInterval. The recommended duration is the duration of one training epoch between the device-cloud(unit: milliseconds). It is used to prevent replay attacks. This parameter is used in the Android environment.
-
-- Parameter
-    - `validInterval`: the valid iteration interval validIterInterval.
-
-## getThreadNum
-
-```java
-public int getThreadNum()
-```
-
-Obtains the number of threads used in federated learning training and inference. The default value is 1.
-
-- Return value
-
-    The number of threads used in federated learning training and inference, which is of the int type.
-
-## setThreadNum
-
-```java
-public void setThreadNum(int threadNum)
-```
-
-Used to set the number of threads used in federated learning training and inference.
-
-- Parameter
-    - `threadNum`: the number of threads used in federated learning training and inference.
-
-## getCpuBindMode
-
-```java
-public int getCpuBindMode()
-```
-
-Obtains the cpu core that threads need to bind during federated learning training and inference.
-
-- Return value
-
-    Convert the enumerated type of cpu core to int type and return.
-
-## setCpuBindMode
-
-```java
-public void setCpuBindMode(BindMode cpuBindMode)
-```
-
-Used to set the cpu core that threads need to bind during federated learning training and inference.
-
-- Parameter
-    - `cpuBindMode`: it is the enumeration type `BindMode`, where BindMode.NOT_BINDING_CORE represents the unbound core, which is automatically assigned by the system, BindMode.BIND_LARGE_CORE represents the bound large core, and BindMode.BIND_MIDDLE_CORE represents the bound middle core.
-
-## getHybridWeightName
-
-```java
-public List getHybridWeightName(RunType runType)
-```
-
-Used in hybrid training mode. Get the training weight name and inference weight name set by the user.
-
-- Parameter
-
-- `runType`: RunType enumeration type, only supports to be set to RunType.TRAINMODE (representing the training weight name) , RunType.INFERMODE (representing the inference weight name).
-
-- Return value
-
-    A list of corresponding weight names according to the parameter runType, which is of the List type.
-
-## setHybridWeightName
-
-```java
-public void setHybridWeightName(List hybridWeightName, RunType runType)
-```
-
-Due to the hybrid training mode, part of the weights delivered by the server is imported into the training model, and part is imported into the inference model, but the framework itself cannot judge it, so the user needs to set the relevant training weight name and inference weight name by himself. This method is provided for the user to set.
-
-- Parameter
-    - `hybridWeightName`: a list of weight names of the List type.
-    - `runType`: RunType enumeration type, only supports setting to RunType.TRAINMODE (representing setting training weight name), RunType.INFERMODE (representing setting reasoning weight name).
-
-## getDataMap
-
-```java
-public Map> getDataMap()
-```
-
-Obtains the federated learning dataset set by the user.
-
-- Return value
-
-    the federated learning dataset set of the Map> type.
-
-## setDataMap
-
-```java
-public void setDataMap(Map> dataMap)
-```
-
-Used to set the federated learning dataset set by the user.
-
-- Parameter
-    - `dataMap`:  the dataset of Map> type, the key in the map is the RunType enumeration type, the value is the corresponding dataset list, when the key is RunType.TRAINMODE, the corresponding value is the training-related dataset list, when the key  is RunType.EVALMODE, it means that the corresponding value is a list of verification-related datasets, and when the key is RunType.INFERMODE, it means that the corresponding value is a list of inference-related datasets.
-
-## getServerMod
-
-```java
-public ServerMod getServerMod()
-```
-
- Obtains the federated learning training mode.
-
-- Return value
-
-    The federated learning training mode of ServerMod enumeration type.
-
-## setServerMod
-
-```java
-public void setServerMod(ServerMod serverMod)
-```
-
-Used to set the federated learning training mode.
-
-- Parameter
-    - `serverMod`:  the federated learning training mode of ServerMod enumeration type, where ServerMod.FEDERATED_LEARNING represents the normal federated learning mode (training and inference use the same model) ServerMod.HYBRID_TRAINING represents the hybrid learning mode (training and inference use different models, and the server side also includes training process).
-
-## getBatchSize
-
-```java
-public int getBatchSize()
-```
-
-Obtains the number of single-step training samples used in federated learning training and inference, that is, batch size.
-
-- Return value
-
-    BatchSize, the number of single-step training samples of int type.
-
-## setBatchSize
-
-```java
-public void setBatchSize(int batchSize)
-```
-
-Used to set the number of single-step training samples used in federated learning training and inference, that is, batch size. It needs to be consistent with the batch size of the input data of the model.
-
-- Parameter
-    - `batchSize`:  the number of single-step training samples of int type.
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/java_api_syncfljob.md b/docs/federated/docs/source_en/java_api_syncfljob.md
deleted file mode 100644
index 67d7939655..0000000000
--- a/docs/federated/docs/source_en/java_api_syncfljob.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# SyncFLJob
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/java_api_syncfljob.md)
-
-```java
-import com.mindspore.flclient.SyncFLJob
-```
-
-SyncFLJob defines the API flJobRun() for starting federated learning on the device, the API modelInference() for inference on the device, the API getModel() for obtaining the latest model on the cloud, and the API stopFLJob() for stopping federated learning training tasks.
-
-## Public Member Functions
-
-| **Function**                     |
-| -------------------------------- |
-| public FLClientStatus flJobRun() |
-| public int[] modelInference()    |
-| public FLClientStatus getModel() |
-| public void stopFLJob()          |
-
-## flJobRun
-
-```java
-public FLClientStatus flJobRun()
-```
-
-Starts a federated learning task on the device, for specific usage, please refer to the [interface introduction document](https://www.mindspore.cn/federated/docs/en/master/interface_description_federated_client.html).
-
-- Return value
-
-    The status code of the flJobRun request.
-
-## modelInference
-
-```java
-public int[] modelInference()
-```
-
-Starts an inference task on the device, for specific usage, please refer to the [interface introduction document](https://www.mindspore.cn/federated/docs/en/master/interface_description_federated_client.html).
-
-- Return value
-
-  int[] composed of the labels inferred from the input.
-
-## getModel
-
-```java
-public FLClientStatus getModel()
-```
-
-Obtains the latest model on the cloud, for specific usage, please refer to the [interface introduction document](https://www.mindspore.cn/federated/docs/en/master/interface_description_federated_client.html).
-
-- Return value
-
-  The status code of the getModel request.
-
-## stopFLJob
-
-```java
-public void stopFLJob()
-```
-
-The training task can be stopped by calling this interface during the federated learning training process.
-
-When a thread calls SyncFLJob.flJobRun(), it can use another thread to call SyncFLJob.stopFLJob() to stop the federated learning training task during the federated learning training process.
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/local_differential_privacy_eval_laplace.md b/docs/federated/docs/source_en/local_differential_privacy_eval_laplace.md
deleted file mode 100644
index 878f3bf693..0000000000
--- a/docs/federated/docs/source_en/local_differential_privacy_eval_laplace.md
+++ /dev/null
@@ -1,236 +0,0 @@
-# Horizontal Federated-Local Differential Privacy Inference Result Protection
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/local_differential_privacy_eval_laplace.md)
-
-## Privacy Protection Background
-
-Evaluating the federated unsupervised model training can be judged by the $loss$ of end-side feedback, or the end-side inference results combined with cloud-side clustering and clustering evaluation metrics can be used to further monitor the federated unsupervised model training progress. The latter involves end-side inference data on the cloud, and in order to meet privacy protection requirements, privacy protection processing of end-side inference data is required, while the cloud side can still be evaluated for clustering. This task is a secondary task compared to the training task, so we use lightweight algorithms and cannot introduce privacy protection algorithms with higher computational or communication overhead than the training phase. This paper presents a lightweight scheme for protecting inference results by using the local differential privacy Laplace noise mechanism.
-
-The effective integration of privacy protection technology into the product services will, on the one hand, help enhance the trust of users and the industry in the products and technology, and on the other hand, help to better carry out the federated tasks under the current privacy compliance requirements and create a full lifecycle (training-inference-evaluation) of privacy protection.
-
-## Algorithm Analysis
-
-### $L1$ and $L2$ Paradigm
-
-The $L1$-norm of a vector $V$ with length $k$ is $||V|||_1=\sum^{k}_{i=1}{|V_i|}$, then the $L1$-norm of the difference between two vectors in two-dimensional space is the Manhattan distance.
-
-$L2$-norm is $||V||_2=\sqrt{\sum^{k}_{i=1}{V^2_i}}$.
-
-The inference result is generally a $softmax$ result with a sum of $1$, and each dimension value of the vector indicates the probability of belonging to the corresponding category of that dimension.
-
-### $L1$ and $L2$ Sensitivity
-
-Local differential privacy introduces uncertainty on the data to be uploaded, and the sensitivity describes an upper bound on the uncertainty. Gaussian noise with $L2$ sensitivity can be added to the gradient in the optimizer and federated training, since a cropping operation is performed on the gradient vector before addition. Here the $softmax$ inference result satisfies the sum as $1$, so the Laplace noise of $L1$ is added. For applications where the $L2$ sensitivity is much lower than the $L1$ sensitivity, the Gaussian mechanism allows to add less noise, but the scenario has no $L2$-related constraint limits and uses only the $L1$ sensitivity.
-
-The $L1$-sensitivity is expressed as the maximum distance for any input in the defined domain in local differential privacy:
-
-$\Delta f=max||X-Y||_1$
-
-In this scenario, $X=, Y=, \sum X = 1, \sum Y = 1, |x_1-y_1|+|x_2-y_2|+...+|x_k-y_k|\leq1=\Delta f$.
-
-### Laplace Distribution
-
-The Laplace distribution is continuous, and the probability density function of the Laplace with mean value 0 is:
-
-$Lap(x|b)=\frac{1}{2b}exp(-\frac{|x|}{b})$
-
-### Laplace Mechanism
-
-$M(x,\epsilon)=X+Lap(\Delta f/\epsilon)$
-
-where $Lap(\Delta f/\epsilon)$ is a vector of random variables with the same shape as $X$, independently and identically distributed.
-
-In this scenario, $b$ (also called $scale$, $lambda$, $beta$) is $1/\epsilon$.
-
-### Proving that the Laplace Mechanism is Satisfied with the $\epsilon-LDP$
-
-Any two different clients, after being processed by the Laplace mechanism, both output the same result to achieve the confusion indistinguishable and the purpose probability ratio of outputting the same result has upper exact bound. Substituting $b=\Delta f/\epsilon$ yields:
-
-$Lap(\Delta f/\epsilon)=\frac{\epsilon}{2\Delta f}exp(-\frac{\epsilon|x|}{\Delta f})$
-
-$\frac{P(Z|X)}{P(Z|Y)}$
-
-$=\prod^k_{i=1}(\frac{exp(-\frac{\epsilon|x_i-z_i|}{\Delta f})}{exp(-\frac{\epsilon |y_i-z_i|}{\Delta f})})$
-
-$=\prod^k_{i=1}exp(\epsilon\frac{|x_i-z_i|-|y_i-z_i|}{\Delta f})$
-
-$\leq\prod^k_{i=1}(\epsilon\frac{|x_i-y_i|}{\Delta f})$
-
-$=exp(\epsilon\frac{X-Y}{\Delta f})$
-
-$\leq exp(\epsilon)$
-
-#### The Determination of $\epsilon$ with the Corresponding Probability Density Plot
-
-The privacy budget with high availability is calculated by combining the data characteristics, such as the requirement to output noise of the order of $1e-5$ with high probability, otherwise it will directly affect the clustering results. The privacy budget calculation method corresponding to generating the specified amount of noise is given below.
-
-There is the $90\%$ probability to output the magnitude of $1e-5$, and the value of $\epsilon$ is obtained by integrating the probability density curve.
-
-$x>=0, Lap(x|b)=\frac{1}{2b}exp(-\frac{x}{b})$
-
-$\int^ {E^{-5}}_0 {Lap(x|b)dx}$
-
-$=1-\frac{1}{2}exp(-\frac{x}{b})|^{E^{-5}}_{0}$
-
-$=\frac{1}{2}(exp(0)-exp(-\frac{E^{-5}}{b}))$
-
-$=0.5(1-exp(-\frac{E^{-5}}{b})) = 0.45$
-
-i.e.
-
-$exp(-\frac{E^{-5}}{b})=0.1$
-
-$b=-E^{-5}/ln(0.1)=E^{-5}/2.3026=1/\epsilon$
-
-$\epsilon=2.3026E^5$
-
-When the privacy budget takes this value, the Laplace probability density function is as follows:
-
-![laplace](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/laplace_pdf.png)
-
-### Impact Analysis of Clustering Evaluation Indicators
-
-Using the **Calinski-Harabasz Index** assessment method as an example, the evaluation indicator is calculated in two steps:
-
-1. Each class calculates the sum of the squares of the distances from all `points` in the class to the `center of the class`;
-
-2. Calculate the sum of squares of distances from each `class` to the `center of the class`;
-
-Source code implementation and impact analysis after noise addition:
-
-```python
-# 1.The cloud-side clustering algorithm gets the class ordinal number to which it belongs, with impact
-n_labels = argmax(X)
-
-extra_disp, intra_disp = 0.0, 0.0
-# 2.Calculate the class center of all points, without impact
-mean = np.mean(X, axis=0)
-for k in range(n_labels):
-    # 3.Get all points in class k, based on the effect of 1
-    cluster_k = X[labels == k]
-    # 4.Get the class center, based on the impact of 1
-    mean_k = np.mean(cluster_k, axis=0)
-    # 5.The distance between the class and the center of all classes, based on the impact of 1
-    extra_disp += len(cluster_k) * np.sum((mean_k - mean) ** 2)
-    # 6.The distance from the point to the center of the class, with impact
-    intra_disp += np.sum((cluster_k - mean_k) ** 2)
-
-return (
-    1.0
-    if intra_disp == 0.0
-    else extra_disp * (n_samples - n_labels) / (intra_disp * (n_labels - 1.0))
-)
-```
-
-In a comprehensive analysis, the main impact is on the clustering algorithm after noise addition, and the error on the distance calculation. When calculating the class center, the error introduced is small because the noise sum is  expected to be $0$.
-
-Taking **SILHOUETTE SCORE** as an example, the process of calculating this evaluation indicator is divided into two steps:
-
-1. Calculate the average distance of a sample point $i$ from all other sample points in the same cluster, which is denoted as $a_i$. The smaller the value is, the more the sample $i$ should be assigned to this cluster.
-
-2. Calculate the average distance $b_{ij}$ of sample $i$ to all samples of some other cluster $C_j$, which is called the dissimilarity of sample $i$ to cluster $C_j$. The inter-cluster dissimilarity of sample $i$ is defined as: $b_i = min(b_{i1}, b_{i2}, ..., b_{ik})$. The larger the value is, the less the sample $i$ should belong to this cluster.
-
-![flow](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/two_cluster.png)
-
-$s_i=(b_i-a_i) / max(a_i, b_i)$.
-
-The smaller $a_i$ is, the larger $b_i$ is, and the result is $1-a_i / b_i$. The closer to $1$, the better the clustering effect.
-
-Pseudocode implementation and impact analysis after noise addition:
-
-```c++
-// Calculate distance matrix, space for time, upper triangle storage, which has an impact on noise addition
-euclidean_distance_matrix(&distance_matrix, group_ids);
-
-// Perform the same calculation for each point, and finally calculate the mean value
-for (size_t i = 0; i < n_samples; ++i) {
-    std::unordered_map> b_i_map;
-    for (size_t j = 0; j < n_samples; ++j) {
-        size_t label_j = labels[j];
-        float distance = distance_matrix[i][j];
-        // Same cluster calculates ai
-        if (label_j == label_i) {
-        a_distances.push_back(distance);
-        } else {
-            // Different clusters calculate bi
-            b_i_map[label_j].push_back(distance);
-        }
-    }
-    if (a_distances.size() > 0) {
-        // Calculate the average distance of the point from other points in the same cluster
-        a_i = std::accumulate(a_distances.begin(), a_distances.end(), 0.0) / a_distances.size();
-    }
-    for (auto &item : b_i_map) {
-        auto &b_i_distances = item.second;
-        float b_i_distance = std::accumulate(b_i_distances.begin(), b_i_distances.end(), 0.0) / b_i_distances.size();
-        b_i = std::min(b_i, b_i_distance);
-    }
-    if (a_i == 0) {
-        s_i[i] = 0;
-    } else {
-        s_i[i] = (b_i - a_i) / std::max(a_i, b_i);
-    }
-}
-return std::accumulate(s_i.begin(), s_i.end(), 0.0) / n_samples;
-```
-
-As above, the main impact is the main impact is on the clustering algorithm after noise addition, and the error on the distance calculation.
-
-### End-side Java Implementation
-
-There is no function in the Java basic library to generate Laplace distributed random numbers. The following combination strategy of random numbers is used to generate.
-
-The source code is as follows:
-
-```java
-float genLaplaceNoise(SecureRandom secureRandom, float beta) {
-    float u1 = secureRandom.nextFloat();
-    float u2 = secureRandom.nextFloat();
-    if (u1 <= 0.5f) {
-        return (float) (-beta * log(1. - u2));
-    } else {
-        return (float) (beta * log(u2));
-    }
-}
-```
-
-After obtaining a new round of model on the end-side, the inference calculation is executed immediately. After the training, the inference results after privacy protection are uploaded to the cloud side together with the new model, and the cloud side finally performs operations such as clustering and score calculation. The flow is shown in the following figure, where the red part is the output result of privacy protection processing:
-
-![flow](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/eval_flow.png)
-
-## Quick Start
-
-### Preparation
-
-To use this feature, one first needs to successfully complete the training aggregation process for either end-cloud federated scenario. [Implementing an Image Classification Application of Cross-device Federated Learning (x86)](https://www.mindspore.cn/federated/docs/en/master/image_classification_application.html) details the preparation of datasets and network models, as well as simulates the process of initiating multi-client participation in federated learning.
-
-### Configuration Items
-
-The [cloud-side yaml configuration file](https://gitee.com/mindspore/federated/blob/master/tests/st/cross_device_cloud/default_yaml_config.yaml) gives the complete configuration items for opening the end-cloud federated, and the program involves the following additional configuration file items:
-
-```c
-encrypt:
-    privacy_eval_type: LAPLACE
-    laplace_eval:
-        laplace_eval_eps: 230260
-```
-
-where `privacy_eval_type` currently supports only `NOT_ENCRYPT` and `LAPLACE`, indicating that the inference results are processed without privacy protection methods and with the `LAPLACE` mechanism, respectively.
-
-`laplace_eval_eps` indicates how much of the privacy budget is used if `LAPLACE` processing is used.
-
-## Experimental Results
-
-The basic configuration associated with the inference result evaluation function is used as follows:
-
-```c
-unsupervised:
-  cluster_client_num: 1000
-  eval_type: SILHOUETTE_SCORE
-```
-
-We can see that the relationship between $loss$ and the score under the `LAPLACE` mechanism by using `NOT_ENCRYPT` and using `laplace_eval_eps=230260` is shown in the figure:
-
-![flow](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/SILHOUETTE.png)
-
-The red dashed line shows the SILHOUETTE scores after the Laplace mechanism is used to protect the inference results. Since the model contains $dropout$ and Gaussian input, the $loss$ of the two trainings are slightly different and the scores obtained based on different models are slightly different. However, the overall trend remains consistent and can assist $loss$ together to detect the model training progress.
diff --git a/docs/federated/docs/source_en/local_differential_privacy_training_noise.md b/docs/federated/docs/source_en/local_differential_privacy_training_noise.md
deleted file mode 100644
index 457d3624c4..0000000000
--- a/docs/federated/docs/source_en/local_differential_privacy_training_noise.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Horizontal FL-Local Differential Privacy Perturbation Training
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/local_differential_privacy_training_noise.md)
-
-During federated learning, user data is used only for local device training and does not need to be uploaded to the central server. This prevents personal data leakage.
-However, in the conventional federated learning framework, models are migrated to the cloud in plaintext. There is still a risk of indirect disclosure of user privacy.
-After obtaining the plaintext model uploaded by a user, the attacker can restore the user's personal training data through attacks such as reconstruction and model inversion. As a result, user privacy is disclosed.
-
-As a federated learning framework, MindSpore Federated provides secure aggregation algorithms based on local differential privacy (LDP). Noise addition is performed on local models before they are migrated to the cloud. On the premise of ensuring the model availability, the problem of privacy leakage in horizontal federated learning is solved.
-
-## Principles
-
-Differential privacy is a mechanism for protecting user data privacy. Differential privacy is defined as follows:
-
-$$
-Pr[\mathcal{K}(D)\in S] \le e^{\epsilon} Pr[\mathcal{K}(D') \in S]+\delta​
-$$
-
-For datasets $D, D'$ that have only one record difference, the random algorithm $\mathcal{K}$ is used to compute the probability of the $S$ subset, which meets the preceding formula. $\epsilon$ is the differential privacy budget, and $\delta$ is the perturbation. The smaller the values of $\epsilon$ and $\delta$, the closer the data distribution of $\mathcal{K}$ on $D$ and $D'$.
-
-In horizontal federated learning, if the model weight matrix after local training on the client is $W$, the attacker can use $W$ to restore the training dataset[1] of the user because the model "remembers" the features of the training set during the training process.
-
-MindSpore Federated provides a LDP-based secure aggregation algorithm to prevent privacy data leakage when local models are migrated to the cloud.
-
-The MindSpore Federated client generates a differential noise matrix $G$ that has the same dimension as the local model $W$, and then adds the two to obtain a weight $W_p$ that meets the differential privacy definition:
-
-$$
-W_p=W+G
-$$
-
-The MindSpore Federated client uploads the noise-added model $W_p$ to the cloud server for federated aggregation. The noise matrix $G$ is equivalent to adding a layer of mask to the original model, which reduces the risk of sensitive data leakage from models and affects the convergence of model training. How to achieve a better balance between model privacy and usability is still a question worth studying. Experiments show that when the number of participants $n$ is large enough (generally more than 1000), most of the noises can cancel each other, and the LDP mechanism has no obvious impact on the accuracy and convergence of the aggregation model.
-
-## Usage
-
-Local differential privacy training currently only supports cross device scenarios. Enabling differential privacy training is simple. You only need to set the `encrypt_train_type` field to `DP_ENCRYPT` via [yaml](https://www.mindspore.cn/federated/docs/en/master/horizontal/federated_server_yaml.html#) when starting the cloud-side service.
-
-In addition, to control the effect of privacy protection, three parameters are provided: `dp_eps`, `dp_delta`, and `dp_norm_clip`. They are also set through the yaml file.
-
-The valid value range of `dp_eps` and `dp_norm_clip` is greater than 0. The legal range of `dp_delta` is 0<`dp_delta`<1. In general, the smaller `dp_eps` and `dp_delta` are, the better the privacy protection will be, but the greater the impact on the convergence of the model. It is recommended that `dp_delta` be taken as the inverse of the number of clients and `dp_eps` be greater than 50.
-
-`dp_norm_clip` is the adjustment coefficient of the model weight before noise is added to the model weight by the LDP mechanism. It affects the convergence of the model. The recommended value ranges from 0.5 to 2.
-
-## References
-
-[1] Ligeng Zhu, Zhijian Liu, and Song Han. [Deep Leakage from Gradients](http://arxiv.org/pdf/1906.08935.pdf). NeurIPS, 2019.
diff --git a/docs/federated/docs/source_en/local_differential_privacy_training_signds.md b/docs/federated/docs/source_en/local_differential_privacy_training_signds.md
deleted file mode 100644
index 5d47104608..0000000000
--- a/docs/federated/docs/source_en/local_differential_privacy_training_signds.md
+++ /dev/null
@@ -1,178 +0,0 @@
-# Horizontal FL-Local Differential Privacy SignDS training
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/local_differential_privacy_training_signds.md)
-
-## Privacy Protection Background
-
-Federated learning enables the client user to participate in global model training without uploading the original dataset by allowing the participant to upload only the new model after local training or update the update information of the model, breaking through the data silos. This common scenario of federated learning corresponds to the default scheme in the MindSpore federated learning framework, where the `encrypt_train_type` switch defaults to `not_encrypt` when starting the `server`. The `installation and deployment` and `application practices` in the federated learning tutorial both use this approach by default, which is a common federated seeking averaging scheme without any privacy-protecting treatment such as cryptographic perturbation. For the convenience of description, `not_encrypt' is used below to refer specifically to this default scheme.
-
-This federated learning scheme is not free from privacy leakage, using the above `not_encrypt` scheme for training. The Server receives the local training model uploaded by the Client, which can still reconstruct the user training data through some attack methods [1], thus leaking user privacy, so the `not_encrypt` scheme needs to further increase the user privacy protection mechanism.
-
-The global model `oldModel` received by the Client in each round of federated learning is issued by the Server, which does not involve user privacy issues. However, the local model `newModel` obtained by each Client after several epochs of local training fits its local privacy data, so the privacy protection focuses on the weight difference between the two `newModel`-`oldModel`=`update`.
-
-The `DP_ENCRYPT` differential noise scheme already implemented in the MindSpore Federated framework achieves privacy preservation by iteratively perturbing Gaussian random noise to `update`. However, as the dimensionality of the model increases, the increase in the `update` paradigm will increase the noise, thus requiring more Clients to participate in the same round of aggregation to neutralize the noise impact, otherwise the convergence and accuracy of the model will be reduced. If the noise is set too small, although the convergence and accuracy are close to the performance of the `not_encrypt` scheme, the privacy protection is not strong enough. Also each Client needs to send the perturbed model, and as the model increases, the communication overhead increases. We expect the Client represented by the cell phone to achieve convergence of the global model with as little communication overhead as possible.
-
-## Algorithm Flow Introduction
-
-SignDS [2] is the abbreviation of Sign Dimension Select, and the processing object is the `update` of Client. Preparation: each layer of Tensor of `update` is flattened and expanded into a one-dimensional vector, connected together, and the number of splicing vector dimensions is noted as $d$.
-
-One sentence summarizes the algorithm: Each participant only uploads information about the important dimensions, including their gradient directions and privacy-preserving steps, which corresponds to the SignDS and MagRR (Magnitude Random Response) modules in the figure below, respectively.
-
-![img](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/signds_framework.png)
-
-Here is an example: there are 3 clients Client1, 2, 3, whose `update` is a $d=8$-dimensional vector after flattening and expanding, and the Server calculates the `avg` of these 3 clients Client and updates the global model with the value, that is, completes a round of federated learning.
-
-| Client | d_1  | d_2  | d_3  | d_4  | d_5  | d_6  | d_7  |  d_8  |
-| :----: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :---: |
-|   1    | 0.4  | 0.1  | -0.2 | 0.3  | 0.5  | 0.1  | -0.2 | -0.3  |
-|   2    | 0.5  | 0.2  |  0   | 0.1  | 0.3  | 0.2  | -0.1 | -0.2  |
-|   3    | 0.3  | 0.1  | -0.1 | 0.5  | 0.2  | 0.3  |  0   |  0.1  |
-|  avg   | 0.4  | 0.13 | -0.1 | 0.3  | 0.33 | 0.2  | -0.1 | -0.13 |
-
-### SignDS
-
-The dimension with higher importance should be selected, and the importance measure is the size of the **fetching value**, and the update needs to be sorted. update takes positive and negative values to represent different update directions, so in each round of federated learning, the sign values of Client each have **0.5 probability** of taking `1` or `-1`. If sign=1, the largest $k$ number of `update` dimensions are noted as the `topk` set and the remaining ones are noted as the `non-topk` set. If sign=-1, the smallest $k$ number of ones are noted as the `topk` set.
-
-If the Server specifies `h`, the total number of selected dimensions, the Client will directly use this value, otherwise each Client will locally calculate the optimal output dimension `h`.
-
-The SignDS algorithm outputs the number of dimensions (denoted as $v$) that should be selected from the `topk` set and the `non-topk` set, as in the example in the table below, where the two sets pick a total of dimensions h=3.
-
-Client selects dimensions uniformly and randomly according to the number of dimensions output by the SignDS algorithm, sends the dimension number and sign value to the Server. If the dimension number is output in the order of picking from `topk` first and then from `non-topk`, the dimension number list `index` needs to be shuffled and disordered. The following table shows the part of information finally transferred from each Client of this algorithm to the Server.
-
-| Client | index | sign |
-| :----: | :---: | :--: |
-|   1    | 1,5,8 |  1   |
-|   2    | 2,3,4 |  -1  |
-|   3    | 3,6,7 |  1   |
-
-### MagRR
-
-The Server receives the dimension direction from the client, but it is not clear what the step size to update in that direction is. Generally speaking, the step length tends to be large at the beginning of training, and shrinks as the training gradually converges. The general trend of step length change is shown in the following figure:
-
-![img](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/signds_step_length.png)
-
-The Server wants to estimate a dynamic range $[0,2∗r_{est}]$ for the actual step $r$, and thus compute the global learning rate $lr_{global}=2∗r_{est}*num_{clients}$.
-
-The $r$ adjustment uses a similar dichotomous idea. The specific process is as follows:
-
-1. The server initializes a smaller $r_{est}$ before the start of training (which does not affect the direction of model convergence too much);
-2. After each round of local training, the participant calculates the true magnitude $r$ (mean of topk dimensions) and converts $r$ to $b$ with certain rules based on the current $r_{est}$ issued from the cloud side;
-3. The participant performs local differential Binary Randomized Response (BRR) perturbation on $b$ and upload the results.
-
-The whole training process is divided into two phases, namely the **fast growth** phase and the **contraction** phase. The rules for $r \rightarrow b$ conversions and server-side updates of $r_{est}$ are slightly different for the participant in the two phases:
-
-- In the fast growth phase, a smaller $r_{est}$ is chosen, such as $e^{-5}$. At this point, $r_{est}$ is expanded by a certain multiple.
-  Therefore, we can define:
-
-  $$
-  b = \begin{cases}
-    0 & r \in [2*r_{est}, \infty] \\
-    1 & r \in [0,2*r_{est})]
-    \end{cases}
-  $$
-
-  The server aggregates all device-side random response results for frequency statistics and calculates the plurality $B$.
-  If $B=0$, it is considered that $r_{est}$ has not reached the range of 𝑟 at present and needs to continue increasing $r_{est}$;
-  If $B=1$, $r_{est}$ is considered to have reached the range of 𝑟, and keep $r_{est}$ unchanged.
-- In the contraction phase, it is necessary to fine-tune $r_{est}$ according to the changes in $r$. Therefore we can define:
-
-  $$
-  b = \begin{cases}
-    0 & r \in [r_{est}, \infty] \\
-    1 & r \in [0,r_{est})]
-    \end{cases}
-  $$
-
-  Calculate $B$, and if $B=0$ , consider that $r_{est}$ and $r$ are currently closer and keep $r_{est}$ unchanged;
-  If $B=1$, $r$ is considered to be generally smaller than $r_{est}$, and $r_{est}$ is halved.
-
-The Server constructs `update` with privacy protection based on the dimension serial number, sign value and $r_{est}$ uploaded by each Client, and aggregates and averages all `update` and updates the current `oldModel` to complete one round of federated learning. The following table shows the aggregation when $2∗r_{est}*num_{clients}=1$.
-
-| Client |  d_1  |  d_2   |  d_3   |  d_4   |  d_5  |  d_6  |  d_7  |  d_8  |
-| :----: | :---: | :----: | :----: | :----: | :---: | :---: | :---: | :---: |
-|   1    | **1** |   0    |   0    |   0    | **1** |   0   |   0   | **1** |
-|   2    |   0   | **-1** | **-1** | **-1** |   0   |   0   |   0   |   0   |
-|   3    |   0   |   0    | **1**  |   0    |   0   | **1** | **1** |   0   |
-|  avg   |  1/3  |  -1/3  |   0    |  -1/3  |  1/3  |  1/3  |  1/3  |  1/3  |
-
-The SignDS scheme enables the device-side client to upload only a list of dimensional ordinal numbers of type int output by the algorithm, a random Sign value of type boolean and feedback results on the estimated value to the cloud side, which significantly reduces the communication overhead compared to uploading tens of thousands of float-level complete model weights or gradients in a common scenario. From the perspective of the actual reconstruction attack, the cloud side only obtains the dimension serial number, a Sign value representing the direction of gradient update and the step estimation feedback value for privacy protection, and the attack is more difficult to achieve. The data flow fields of the overall scheme are shown in the following figure:
-
-![img](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/signds_flow.png)
-
-## Privacy Protection Certificate
-
-The differential privacy noise scheme achieves privacy protection by adding noise so that the attacker cannot determine the original information, while the differential privacy SignDS scheme activates partial dimensions and replaces the original value with the sign value, which largely protects user privacy. Further, using the differential privacy index mechanism makes it impossible for an attacker to confirm whether the activated dimensions are significant (from the `topk` set) and whether the number of dimensions from `topk` in the output dimensions exceeds a given threshold.
-
-### Dimensional Selection Mechanism Based on Index Mechanism
-
-For any two updates $\Delta$ and $\Delta'$ of each Client, the set of `topk` dimensions is $S_{topk}$ , ${S'}_{topk}$ , respectively. The set of any possible output dimensions of the algorithm is ${J}\in {\mathcal{J}}$ . Note that $\nu=|{S}_ {topk}\cap {J}|$ , $\nu'=|{S'}_{topk}\cap {J}|$ is the number of intersections of ${J}$ and `topk` sets, and the algorithm such that the following inequality holds:
-
-$$
-\frac{{Pr}[{J}|\Delta]}{{Pr}[{J}|\Delta']}=\frac{{Pr}[{J}|{S}_{topk}]}{{Pr}[{J}|{S'}_{topk}]}=\frac{\frac{{exp}(\frac{\epsilon}{\phi_u}\cdot u({S}_{topk},{J}))}{\sum_{{J'}\in {\mathcal{J}}}{exp}(\frac{\epsilon}{\phi_u}\cdot u({S}_{topk}, {J'}))}}{\frac{{exp}(\frac{\epsilon}{\phi_u}\cdot u({S'}_{topk}, {J}))}{\sum_{ {J'}\in {\mathcal{J}}}{exp}(\frac{\epsilon}{\phi_u}\cdot u( {S'}_{topk},{J'}))}}=\frac{\frac{{exp}(\epsilon\cdot \unicode{x1D7D9}(\nu \geq \nu_{th}))}{\sum_{\tau=0}^{\tau=\nu_{th}-1}\omega_{\tau} + \sum_{\tau=\nu_{th}}^{\tau=h}\omega_{\tau}\cdot {exp}(\epsilon)}}{\frac{ {exp}(\epsilon\cdot \unicode{x1D7D9}(\nu' \geq\nu_{th}))}{\sum_{\tau=0}^{\tau=\nu_{th}-1}\omega_{\tau}+\sum_{\tau=\nu_{th}}^{\tau=h}\omega_{\tau}\cdot {exp}(\epsilon)}}\\= \frac{{exp}(\epsilon\cdot \unicode{x1D7D9} (\nu \geq \nu_{th}))}{ {exp}(\epsilon\cdot \unicode{x1D7D9} (\nu' \geq \nu_{th}))} \leq \frac{{exp}(\epsilon\cdot 1)}{{exp}(\epsilon\cdot 0)} = {exp}(\epsilon),
-$$
-
-It is proved that the algorithm satisfies local differential privacy.
-
-### Local Differential Privacy-Random Response Mechanism
-
-The participant receives the estimate sent from the server, and after the local training is completed, the topk dimensional weight mean of the real update is calculated, and 0 or 1 is output according to the magRR strategy. We consider that 0 or 1 still carries the weight mean range information, and it needs further protection.
-
-The input of the random response mechanism is the data to be protected ($\b\in \{0,1\}$) and the privacy parameter $\epsilon$, which flips the data according to a certain probability and outputs $\hat{b} \in \{0,1\}$ with the following rules:
-
-$$
-\hat{b} = \begin{cases}
-b & with \quad probability \quad P \\
-1-b & with \quad probability \quad 1-P
-\end{cases}
-$$
-
-where $P=\frac{e^\epsilon}{1+e^\epsilon}$.
-
-#### Frequency Statistics Based on Random Response Mechanism
-
-It is difficult for adversaries to distinguish real data from scrambled data by random responses, but it also affects the availability of cloud-side statistical tasks. The server side can approximate the true statistical frequency values by noise reduction, but it is difficult to infer the true input of the user in reverse. Let $N$ be the total number of participants in a round, $N^T$ be the total number of 1 originally, and $N^C$ be the total number of 1 collected by the server, then we have:
-
-$$
-N^T*P+(N-N^T)*(1-P)=N^C \\
-N^T=\frac{N^C-N+NP}{2P-1}
-$$
-
-## Preparation
-
-To use the algorithm, one first needs to successfully complete the training aggregation process for either cross-device federated scenario. [Implementing an Image Classification Application of Cross-device Federated Learning (x86)](https://www.mindspore.cn/federated/docs/en/master/image_classification_application.html) describes the preparation work such as datasets, network models, and simulations to initiate the process of multi-client participation in federated learning in detail.
-
-## Algorithm Opening Script
-
-Local differential privacy SignDS training currently only supports cross-device federated learning scenarios. The opening method needs to change the following parameter configuration in the yaml file when opening the cloud-side service. The complete cloud-side opening script can be referred to the cloud-side deployment, and the relevant parameter configuration for opening this algorithm is given here. Taking LeNet task as an example, the yaml related configuration is as follows:
-
-```python
-encrypt:
-  encrypt_train_type: SIGNDS
-  ...
-  signds:
-    sign_k: 0.2
-    sign_eps: 100
-    sign_thr_ratio: 0.6
-    sign_global_lr: 0.1
-    sign_dim_out: 0
-```
-
-For the detailed example, refer to [Implementing an Image Classification Application of Cross-device Federated Learning (x86)](https://www.mindspore.cn/federated/docs/en/master/image_classification_application.html). The cloud-side code implementation gives the definition domain of each parameter. If it is not in the definition domain, Server will report an error prompting the definition domain. The following parameter changes are subject to keeping the remaining 4 parameters unchanged.
-
-- `sign_k`: (0,0.25], k*inputDim>50. default=0.01. `inputDim` is the pulling length of the model or update. If not satisfied, there is a device-side warning. Sort update, and the `topk` set is composed of the first k (%) of it. Decreasing k means to pick from more important dimensions with greater probability. The output will have fewer dimensions, but the dimensions are more important and the change in convergence cannot be determined. The user needs to observe the sparsity of model update to determine the value. When it is quite sparse (update has many zeros), it should be taken smaller.
-- `sign_eps`: (0,100], default=100. Privacy-preserving budget. The number sequence symbol is $\epsilon$, abbreviated as eps. When eps decreases, the probability of picking unimportant dimensions increases. When privacy protection is enhanced, output dimensions decrease, the percentage remains the same, and precision decreases.
-- `sign_thr_ratio`: [0.5,1], default=0.6. The dimension from `topk` in the activation dimension is occupied threshold lower bound. Increasing will reduce the output dimension, but the proportion of output dimensions from `topk` will increase. When the value is increased excessively, more from `topk` is required in the output, and the total output dimension can only be reduced to meet the requirement, and the accuracy decreases when the number of clients is not large enough.
-- `sign_global_lr`: (0,), default=1. This value is multiplied by sign instead of update, which directly affects the convergence speed and accuracy. Moderately increasing this value will improve the convergence speed, but it may make the model oscillate and the gradient explode. If more epochs are run locally per client and the learning rate used for local training is increased, the value needs to be increased accordingly. If the number of clients involved in the aggregation increases, the value also needs to be increased, because the value needs to be aggregated and then divided by the number of users when reconstruction. The result will remain the same only if the value is increased. If the percentage of participants in the new version (r0.2) involved in aggregation is less than 5%, the $lr_{global}$ of the MagRR algorithm is directly adjusted to this parameter.
-- `sign_dim_out`: [0,50], default=0. If a non-zero value is given, the client side uses the value directly, increasing the value to output more dimensions, but the proportion of dimensions from `topk` will decrease. If it is 0, the client user has to calculate the optimal output parameters. If eps is not large enough, and the value is increased, many `non-topk` insignificant dimensions will be output leading to affect the mode convergence and accuracy decrease. When eps is large enough, increasing the value will allow important dimension information of more users to leave the local area and improve the accuracy.
-
-## LeNet Experiment Results
-
-Use 100 client datasets of `3500_clients_bin`, 600 iterations of federated aggregation. 20 epochs run locally per client, and using learning rate of device-side local training is 0.01. The related parameter of SignDS is `k=0.2, eps=100, ratio=0.6, lr=4, out=0`, and the variation curves of Loss and Auc are shown in the following figure. In the unencrypted scenario, the length of the data uploaded to the cloud side at the end of training on the device side is 266,084, but the length of the data uploaded by SignDS is only 656.
-
-![loss](./images/lenet_signds_loss_auc.png)
-
-## References
-
-[1] Ligeng Zhu, Zhijian Liu, and Song Han. [Deep Leakage from Gradients](http://arxiv.org/pdf/1906.08935.pdf). NeurIPS, 2019.
-
-[2] Xue Jiang, Xuebing Zhou, and Jens Grossklags. "SignDS-FL: Local Differentially-Private Federated Learning with Sign-based Dimension Selection." ACM Transactions on Intelligent Systems and Technology, 2022.
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/object_detection_application_in_cross_silo.md b/docs/federated/docs/source_en/object_detection_application_in_cross_silo.md
deleted file mode 100644
index 6655d58eb1..0000000000
--- a/docs/federated/docs/source_en/object_detection_application_in_cross_silo.md
+++ /dev/null
@@ -1,264 +0,0 @@
-# Implementing a Cross-Silo Federated Target Detection Application (x86)
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/object_detection_application_in_cross_silo.md)
-
-Based on the type of participating clients, federated learning can be classified into cross-silo federated learning and cross-device federated learning. In a cross-silo federated learning scenario, the clients involved in federated learning are different organizations (e.g., healthcare or finance) or geographically distributed data centers, i.e., training models on multiple data silos. In the cross-device federated learning scenario, the participating clients are a large number of mobile or IoT devices. This framework will describe how to implement a target detection application by using network Fast R-CNN on MindSpore Federated cross-silo federated framework.
-
-The full script to launch cross-silo federated target detection application can be found [here](https://gitee.com/mindspore/federated/tree/master/example/cross_silo_faster_rcnn).
-
-## Preparation
-
-This tutorial deploy the cross-silo federated target detection task based on the faster_rcnn network provided in MindSpore model_zoo. Please first follow the official [faster_rcnn tutorial and code](https://gitee.com/mindspore/models/tree/master/official/cv/FasterRCNN) to understand the COCO dataset, faster_rcnn network structure, training process and evaluation process first. Since the COCO dataset is open source, please refer to its [official website](https://cocodataset.org/#home) guidelines to download a dataset by yourself and perform dataset slicing (for example, suppose there are 100 clients, the dataset can be sliced into 100 copies, each representing the data held by one client).
-
-Since the original COCO dataset is in json file format, the target detection script provided by cross-silo federated learning framework only supports input data in MindRecord format. You can convert the json file to MindRecord format file according to the following steps.
-
-- Configure the following parameters in the configuration file[default_config.yaml](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_faster_rcnn/default_config.yaml):
-
-    - `mindrecord_dir`
-
-        Used to set the generated MindRecord format file save path. The folder name must be mindrecord_{num} format, and the number num represents the client label number 0, 1, 2, 3, ......
-
-        ```sh
-        mindrecord_dir:"./datasets/coco_split/split_100/mindrecord_0"
-        ```
-
-    - `instance_set`
-
-        Used to set original json file path.
-
-        ```sh
-        instance_set: "./datasets/coco_split/split_100/train_0.json"
-        ```
-
-- Run the script [generate_mindrecord.py](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_faster_rcnn/generate_mindrecord.py) to generate MindRecord file according to `train_0.json`, saved in the `mindrecord_dir` path.
-
-## Starting the Cross-Silo Federated Mission
-
-### Installing MindSpore and MindSpore Federated
-
-Including both downloading source code and downloading release version, supporting CPU, GPU, Ascend hardware platforms, just choose to install according to the hardware platforms.  For the installing step, refer to [MindSpore installation](https://www.mindspore.cn/install) and [MindSpore Federated installation](https://www.mindspore.cn/federated/docs/en/master/index.html).
-
-Currently the federated learning framework is only supported for deployment in Linux environments, and cross-silo federated learning framework requires MindSpore version number >= 1.5.0.
-
-### Starting Mission
-
-Refer to [example](https://gitee.com/mindspore/federated/tree/master/example/cross_silo_faster_rcnn) to start the cluster. The reference example directory structure is as follows:
-
-```text
-cross_silo_faster_rcnn
-├── src
-│   ├── FasterRcnn
-│   │   ├── __init__.py                  // init file
-│   │   ├── anchor_generator.py          // Anchor generator
-│   │   ├── bbox_assign_sample.py        // Phase I Sampler
-│   │   ├── bbox_assign_sample_stage2.py // Phase II Sampler
-│   │   ├── faster_rcnn_resnet.py        // Faster R-CNN network
-│   │   ├── faster_rcnn_resnet50v1.py    // Faster R-CNN network taking Resnet50v1.0 as backbone
-│   │   ├── fpn_neck.py                  // Feature Pyramid Network
-│   │   ├── proposal_generator.py        // Candidate generator
-│   │   ├── rcnn.py                      // R-CNN network
-│   │   ├── resnet.py                    // Backbone network
-│   │   ├── resnet50v1.py                // Resnet50v1.0 backbone network
-│   │   ├── roi_align.py                 // ROI aligning network
-│   │   └── rpn.py                       // Regional candidate network
-│   ├── dataset.py                     // Create and process datasets
-│   ├── lr_schedule.py                 // Learning rate generator
-│   ├── network_define.py              // Faster R-CNN network definition
-│   ├── util.py                        // Routine operation
-│   └── model_utils
-│           ├── __init__.py                  // init file
-│           ├── config.py                    // Obtain .yaml configuration parameter
-│           ├── device_adapter.py            // Obtain on-cloud id
-│           ├── local_adapter.py             // Get local id
-│           └── moxing_adapter.py            // On-cloud data preparation
-├── requirements.txt
-├── mindspore_hub_conf.py
-├── generate_mindrecord.py              // Convert annotations files in .json format to MindRecord format for reading datasets
-├── default_yaml_config.yaml                 // Required configuration files for Federated training
-├── default_config.yaml                         // Required configuration file of network structure, dataset address, and fl_plan
-├── run_cross_silo_fasterrcnn_worker.py // Start Cloud Federated worker script
-├── run_cross_silo_fasterrcnn_worker_distribute.py // Start the Cloud Federated distributed worker training script
-└── test_fl_fasterrcnn.py               // Training scripts used by the client
-└── run_cross_silo_fasterrcnn_sched.py  // Start Cloud federated scheduler script
-└── run_cross_silo_fasterrcnn_server.py // Start Cloud federated server script
-```
-
-1. Note that you can choose whether to record the loss value for each step by setting the parameter `dataset_sink_mode` in the `test_fl_fasterrcnn.py` file.
-
-    ```python
-    model.train(config.client_epoch_num, dataset, callbacks=cb, dataset_sink_mode=True)   # Not setting dataset_sink_mode means that only the loss value of the last step in each epoch is recorded.
-    model.train(config.client_epoch_num, dataset, callbacks=cb, dataset_sink_mode=False)   # Set dataset_sink_mode=False to record the loss value of each step, which is the default mode in the code.
-    ```
-
-2. Set the following parameters in configuration file [default_config.yaml](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_faster_rcnn/default_config.yaml):
-
-    - `pre_trained`
-
-        Used to set the pre-trained model path (.ckpt format).
-
-        The pre-trained model experimented in this tutorial is a ResNet-50 checkpoint trained on ImageNet 2012. You can use the [resnet50](https://gitee.com/mindspore/models/tree/master/official/cv/ResNet) script in ModelZoo to train, and then use src/convert_checkpoint.py to convert the trained resnet50 weight file into a loadable weight file.
-
-3. Start redis
-
-    ```sh
-    redis-server --port 2345 --save ""
-    ```
-
-4. Start Scheduler
-
-    `run_cross_silo_fasterrcnn_sched.py` is the Python script used to start `Scheduler` and supports modifying the configuration by passing argument `argparse`. Execute the following command, which represents the `Scheduler` that starts this federated learning task. `--yaml_config` is used to set the yaml file path, and its management ip:port is `127.0.0.1:18019`.
-
-    ```sh
-    python run_cross_silo_fasterrcnn_sched.py --yaml_config="default_yaml_config.yaml" --scheduler_manage_address="127.0.0.1:18019"
-    ```
-
-    For the detailed implementation, see [run_cross_silo_fasterrcnn_sched.py](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_faster_rcnn/run_cross_silo_fasterrcnn_sched.py).
-
-    The following print represents a successful starting:
-
-    ```sh
-    [INFO] FEDERATED(3944,2b280497ed00,python):2022-10-10-17:11:08.154.878 [mindspore_federated/fl_arch/ccsrc/scheduler/scheduler.cc:35] Run] Scheduler started successfully.
-    [INFO] FEDERATED(3944,2b28c5ada700,python):2022-10-10-17:11:08.155.056 [mindspore_federated/fl_arch/ccsrc/common/communicator/http_request_handler.cc:90] Run] Start http server!
-    ```
-
-5. Start Server
-
-    `run_cross_silo_fasterrcnn_server.py` is a Python script for starting a number of `Server`s, and supports modifying the configuration by passing argument `argparse`. Execute the following command, representing the `Server` that starts this Federated Learning task with a TCP address of `127.0.0.1`. The starting port for the Federated Learning HTTP service is `6668` and the number of `Server`s is `4`.
-
-    ```sh
-    python run_cross_silo_fasterrcnn_server.py --yaml_config="default_yaml_config.yaml" --tcp_server_ip="127.0.0.1" --checkpoint_dir="/path/to/fl_ckpt" --local_server_num=4 --http_server_address="127.0.0.1:6668"
-    ```
-
-    The above command is equivalent to starting four `Server` processes, each with a federated learning service port of `6668`, `6669`, `6670` and `6671`, as detailed in [run_cross_silo_fasterrcnn_server.py](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_faster_rcnn/run_cross_silo_fasterrcnn_server.py), and checkpoint_dir needs to enter the directory path where the checkpoint is located. The server will read the checkpoint initialization weight from this path. The prefix format of the checkpoint needs to be `{fl_name}_ recovery_ iteration_`.
-
-    The following print represents a successful starting:
-
-    ```sh
-    [INFO] FEDERATED(3944,2b280497ed00,python):2022-10-10-17:11:08.154.645 [mindspore_federated/fl_arch/ccsrc/common/communicator/http_server.cc:122] Start] Start http server!
-    [INFO] FEDERATED(3944,2b280497ed00,python):2022-10-10-17:11:08.154.725 [mindspore_federated/fl_arch/ccsrc/common/communicator/http_request_handler.cc:85] Initialize] Ev http register handle of: [/d    isableFLS, /enableFLS, /state, /queryInstance, /newInstance] success.
-    [INFO] FEDERATED(3944,2b280497ed00,python):2022-10-10-17:11:08.154.878 [mindspore_federated/fl_arch/ccsrc/scheduler/scheduler.cc:35] Run] Scheduler started successfully.
-    [INFO] FEDERATED(3944,2b28c5ada700,python):2022-10-10-17:11:08.155.056 [mindspore_federated/fl_arch/ccsrc/common/communicator/http_request_handler.cc:90] Run] Start http server!
-    ```
-
-6. Start Worker
-
-    `run_cross_silo_femnist_worker.py` is a Python script for starting a number of `worker`s, and supports modifying the configuration by the passing argument `argparse`. The following instruction is executed, representing the `worker` that starts this federated learning task, and the number of `workers` needed for the federated learning task to proceed properly is at least `2`.
-
-    ```sh
-    python run_cross_silo_fasterrcnn_worker.py --local_worker_num=2 --yaml_config="default_yaml_config.yaml" --pre_trained="/path/to/pre_trained" --dataset_path=/path/to/datasets/coco_split/split_100 --http_server_address=127.0.0.1:6668
-    ```
-
-    For the detailed implementation, see [run_cross_silo_femnist_worker.py](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_faster_rcnn/run_cross_silo_fasterrcnn_worker.py). Note that in dataset sink mode, the unit of the synchronization frequency of Cloud Federated is in epoch, otherwise the synchronization frequency is in step.
-
-    As the above command, `--local_worker_num=2` means starting two clients, and the datasets used by the two clients are `datasets/coco_split/split_100/mindrecord_0` and `datasets/coco_split/split_100/mindrecord_1`. Please prepare the required datasets for the corresponding clients according to the `pre-task preparation` tutorial.
-
-    After executing the above three commands and waiting for a while, go to the `worker_0` folder in the current directory and check the `worker_0` log with the command `grep -rn "\epoch:" *` and you will see a log message similar to the following:
-
-    ```sh
-    epoch: 1 step: 1 total_loss: 0.6060338
-    ```
-
-    Then it means that cross-silo federated is started successfully and `worker_0` is training. Other workers can be viewed in a similar way.
-
-    At present, the `worker` node of Cloud Federated supports the distributed training mode of single machine multi-card and multi-machine multi-card. `run_cross_silo_fasterrcnn_worker_distributed.py` is a python script for users to start distributed training of the worker node, and supports configuration modification via argparse. Execute the following instructions, representing the distributed `worker` that starts this federated learning task, where `device_num` represents the number of processes started by the `worker` cluster, `run_distribute` represents the distributed training started by the cluster, and its http start port is `6668`. Number of `worker` processes is `4`:
-
-    ```sh
-    python run_cross_silo_fasterrcnn_worker_distributed.py --device_num=4 --run_distribute=True --dataset_path=/path/to/datasets/coco_split/split_100 --http_server_address=127.0.0.1:6668
-    ```
-
-    Enter the `worker_distributed/log_output/` folder in the current directory and run the `grep -rn "epoch" *` command to view the logs of the `worker` distributed cluster. You can see the following information:
-
-    ```sh
-    epoch: 1 step: 1 total_loss: 0.613467
-    ```
-
-    Please refer to [yaml configuration notes](https://www.mindspore.cn/federated/docs/en/master/horizontal/federated_server_yaml.html) for the description of parameter configuration in the above script.
-
-### Viewing the Log
-
-After successfully starting the task, the corresponding log file will be generated under the current directory `cross_silo_faster_rcnn`. The log file directory structure is as follows:
-
-```text
-cross_silo_faster_rcnn
-├── scheduler
-│   └── scheduler.log     # Print logs during running scheduler
-├── server_0
-│   └── server.log        # Print logs during running server_0
-├── server_1
-│   └── server.log        # Print logs during running server_1
-├── server_2
-│   └── server.log        # Print logs during running server_2
-├── server_3
-│   └── server.log        # Print logs during running server_3
-├── worker_0
-│   ├── ckpt              # Store the aggregated model ckpt obtained by worker_0 at the end of each federated learning iteration
-│   │  └── mindrecord_0
-│   │      ├── mindrecord_0-fast-rcnn-0epoch.ckpt
-│   │      ├── mindrecord_0-fast-rcnn-1epoch.ckpt
-│   │      │
-│   │      │              ......
-│   │      │
-│   │      └── mindrecord_0-fast-rcnn-29epoch.ckpt
-│   ├──loss_0.log         # Record the loss value of each step in the training process of worker_0
-│   └── worker.log        # Record the output logs during worker_0 participation in the federal learning task
-└── worker_1
-    ├── ckpt              # Store the aggregated model ckpt obtained by worker_1 at the end of each federated learning iteration
-    │  └── mindrecord_1
-    │      ├── mindrecord_1-fast-rcnn-0epoch.ckpt
-    │      ├── mindrecord_1-fast-rcnn-1epoch.ckpt
-    │      │
-    │      │                     ......
-    │      │
-    │      └── mindrecord_1-fast-rcnn-29epoch.ckpt
-    ├──loss_0.log         # Record the loss value of each step in the training process of worker_1
-    └── worker.log        # Record the output logs during worker_1 participation in the federal learning task
-```
-
-### Closing the Mission
-
-If you want to exit in the middle, the following command is available:
-
-```sh
-python finish_cross_silo_fasterrcnn.py --redis_port=2345
-```
-
-For the detailed implementation, see [finish_cloud.py](https://gitee.com/mindspore/federated/blob/master/tests/st/cross_device_cloud/finish_cloud.py).
-
-Or when the training task is finished, the cluster exits automatically, no need to close it manually.
-
-### Results
-
-- Use data:
-
-  COCO dataset is split into 100 copies, and the first two copies are taken as two worker datasets respectively
-
-- The number of client-side local training epochs: 1
-
-- Total number of cross-silo federated learning iterations: 30
-
-- Results (recording the loss values during the client-side local training):
-
-  Go to the `worker_0` folder in the current directory, and check the `worker_0` log with the command `grep -rn "\]epoch:" *` to see the loss values output in each step:
-
-  ```sh
-  epoch: 1 step: 1 total_loss: 5.249325
-  epoch: 1 step: 2 total_loss: 4.0856013
-  epoch: 1 step: 3 total_loss: 2.6916502
-  epoch: 1 step: 4 total_loss: 1.3917351
-  epoch: 1 step: 5 total_loss: 0.8109232
-  epoch: 1 step: 6 total_loss: 0.99101084
-  epoch: 1 step: 7 total_loss: 1.7741735
-  epoch: 1 step: 8 total_loss: 0.9517553
-  epoch: 1 step: 9 total_loss: 1.7988946
-  epoch: 1 step: 10 total_loss: 1.0213892
-  epoch: 1 step: 11 total_loss: 1.1700443
-                    .
-                    .
-                    .
-  ```
-
-The histograms of the training loss transformations in each step of worker_1 and worker_2 during the 30 iterations training are as follows, [1] and [2]:
-
-The polygrams of the average loss (the sum of the losses of all the steps in an epoch divided by the number of steps) in each step of worker_1 and worker_2 during the 30 iterations training are as follows, [3] and [4]:
-
-![cross-silo_fastrcnn-2workers-loss.png](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/cross-silo_fastrcnn-2workers-loss.png)
diff --git a/docs/federated/docs/source_en/pairwise_encryption_training.md b/docs/federated/docs/source_en/pairwise_encryption_training.md
deleted file mode 100644
index 830d531997..0000000000
--- a/docs/federated/docs/source_en/pairwise_encryption_training.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Horizontal FL-Pairwise Encryption Training
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/pairwise_encryption_training.md)
-
-During federated learning, user data is used only for local device training and does not need to be uploaded to the central server. This prevents personal data leakage.
-However, in the conventional federated learning framework, models are migrated to the cloud in plaintext. There is still a risk of indirect disclosure of user privacy.
-After obtaining the plaintext model uploaded by a user, the attacker can restore the user's personal training data through attacks such as reconstruction and model inversion. As a result, user privacy is disclosed.
-
-As a federated learning framework, MindSpore Federated provides secure aggregation algorithms based on local secure multi-party computation (MPC).
-Secret noise addition is performed on local models before they are migrated to the cloud. On the premise of ensuring the model availability, the problem of privacy leakage and model theft in horizontal federated learning are solved.
-
-## Principles
-
-Although the LDP technology can properly protect user data privacy, when there are a relatively small quantity of participating clients or a Gaussian noise amplitude is relatively large, the model accuracy is greatly affected.
-To meet both model protection and model convergence requirements, we provide the MPC-based secure aggregation solution.
-
-In this training mode, assuming that the participating client set is $U$, for any client $u$ and $v$, they negotiate a pair of random perturbations $p_{uv}$ and $p_{vu}$, which meet the following condition:
-
-$$
-p_{uv}=\begin{cases} -p_{vu}, &u{\neq}v\\\\ 0, &u=v \end{cases}
-$$
-
-Therefore, each client $u$ adds the perturbation negotiated with other users to the original model weight $x_u$ before uploading the model to the server:
-
-$$
-x_{encrypt}=x_u+\sum\limits_{v{\in}U}p_{uv}
-$$
-
-Therefore, the Server aggregation result $\overline{x}$ is as follows:
-
-$$
-\begin{align}
-\overline{x}&=\sum\limits_{u{\in}U}(x_{u}+\sum\limits_{v{\in}U}p_{uv})\\\\
-&=\sum\limits_{u{\in}U}x_{u}+\sum\limits_{u{\in}U}\sum\limits_{v{\in}U}p_{uv}\\\\
-&=\sum\limits_{u{\in}U}x_{u}
-\end{align}
-$$
-
-The preceding process describes only the main idea of the aggregation algorithm. The MPC-based aggregation solution is accuracy-lossless but increases the number of communication rounds.
-If you are interested in the specific steps of the algorithm, refer to the paper[1].
-
-## Usage
-
-### Cross Device Scenario
-
-Enabling pairwise encryption training is simple. Just set the `encrypt_train_type` field to `PW_ENCRYPT` through yaml file when starting the cloud-side service.
-
-In addition, most of the workers participating in the training are unstable edge computing nodes such as mobile phones, so the problems of dropping the line and secret key reconstruction should be considered. Related parameters are `share_secrets_ratio`, `reconstruct_secrets_threshold`, and `cipher_time_window`.
-
-`share_client_ratio` indicates the client threshold decrease ratio of public key broadcast round, secret sharing round and secret reconstruction round. The value must be less than or equal to 1.
-
-`reconstruct_secrets_threshold` indicates the number of secret shares required to reconstruct a secret. The value must be less than the number of clients that participate in updateModel (start_fl_job_threshold*update_model_ratio).
-
-To ensure system security, the value of `reconstruct_secrets_threshold` must be greater than half of the number of federated learning clients when the server and client are not colluded.
-When the server and client are colluded, the value of `reconstruct_secrets_threshold` must be greater than two thirds of the number of federated learning clients.
-
-`cipher_time_window` indicates the duration limit of each communication round for secure aggregation. It is used to ensure that the server can start a new round of iteration when some clients are offline.
-
-### Cross Silo Scenario
-
-In cross silo scenario, you only need to set the `encrypt_train_type` field to `PW_ENCRYPT` through yaml file in the cloud-side startup script.
-
-Different from cross silo scenario, all of the workers are stable computing nodes in cross silo scenario. You only need to set the parameter `cipher_time_window`.
-
-## References
-
-[1] Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, et al. [Practical Secure Aggregationfor Privacy-Preserving Machine Learning](https://dl.acm.org/doi/pdf/10.1145/3133956.3133982). Proceedings of the 2017 ACM SIGSAC Conference on Computer and communications Security. 2017.
diff --git a/docs/federated/docs/source_en/private_set_intersection.md b/docs/federated/docs/source_en/private_set_intersection.md
deleted file mode 100644
index fcb7bef84f..0000000000
--- a/docs/federated/docs/source_en/private_set_intersection.md
+++ /dev/null
@@ -1,129 +0,0 @@
-# Vertical Federated-Privacy Set Intersection
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/private_set_intersection.md)
-
-## Privacy Protection Background
-
-With the rise in demand for digital transformation and the circulation of data elements, as well as the implementation of the Data Security Law, the Personal Information Protection Law and the EU General Data Protection Regulation (GDPR), privacy of data is increasingly becoming a necessary requirement in many scenarios. For example, when the dataset is sensitive information of users (medical diagnosis information, transaction records, identification codes, device unique identifier OAID, etc.) or secret information of the company, cryptography or desensitization must be used to ensure the confidentiality of the data before using it in the open state to achieve the goal of "usable but invisible" of the data in order to prevent information leakage. Considering two participants who jointly train a machine learning model (e.g., vertical federated learning) by using their respective data, the first step of this task is to align the sample sets of both parties, a process known as Entity Resolution. Traditional plaintext intersection inevitably reveals the OAID of the entire database and damages the data privacy of both parties, so the Privacy Set Intersection (PSI) technique is needed to accomplish this task.
-
-PSI is a type of secure multi-party computing (MPC) protocol that takes data collection from two parties as input, after a series of hashing, encryption and data exchange steps, eventually outputs the intersection of the collection to an agreed output party, while ensuring that the participating parties cannot obtain any information about the data outside the intersection. The use of the PSI protocol in vertical federated learning tasks, in compliance with the GDPR requirement of Data Minimisation, i.e. there is no non-essential exposure of data, except for the parts necessary for the training process (intersections). From the data controller's perspective, the service has to share data appropriately, but wants to share only necessary data based on the service and not expose additional data to the public. It should be noted that while PSI can directly apply existing MPC protocols to its calculations, this often results in a large computational and communication overhead, which is not conducive to business. In this paper, we introduce a technique combining Bloom filter and eliminable inverse scalar multiplication on the elliptic curve to implement ECDH-PSI (Elliptic Curve Diffie-Hellman key Exchange-PSI) to better support cloud services and carry out privacy preserving set intersection computing services.
-
-## Algorithm Process Introduction
-
-The core idea of ECDH-PSI is that a piece of data is first encrypted by Alice and then encrypted by Bob, with the same result as exchanging the encryption order. One party sends the data encrypted with its own private key without revealing its privacy, and the other party re-encrypts it with its own private key based on the received encrypted data. If the encryption result is the same, the original data is the same.
-
-The core optimization point of the inverse ECDH-PSI is to minimize the encryption computation based on the set of large amount of data when facing the scenario of intersection between two parties with unbalanced amount of data (Bob is the party with less data, $a$ and $b$ are the private keys of Alice and Bob respectively, the original data of both parties are mapped to the elliptic curve as $P_1$ and $P_2$ respectively, the point multiplication encryption of the elliptic curve with the private key $k$ is $P^k$ or $kP$, and the inverse of the private key $k$ is $k^{-1}$). Then after Alice executes $p_1^a$ and sends it to Bob, Bob no longer performs the encryption calculation based on it, but sends $p_2^b$ to Alice. After Alice sends $P_2^{ba}$, Bob completes the offset operation by point multiplying the inverse of its private key, i.e., calculating $P_2^{bab^{-1}}$ and comparing it with the $P_1^a$ sent by Alice. If the encryption result is the same, it means $P_1=P_2$. The flowchart of the inverse ECDH-PSI is shown in the figure, and the red letters indicate the received data from the other side.
-
-![inverse_ecdh_psi_flow](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/inverse_ecdh_psi_flow.png)
-
-The $bf$ in the figure stands for Bloom filter (bf). If you want to query whether an element exists in a collection, the basic method is to iterate through the collection to query, or sort the collection and use dichotomous lookup to query, but when the amount of data is too large, sorting does not support parallelism, which is very time-consuming. If a bloom filter is used, the elements of the set are mapped to a number of bits in an initial all-0 bit string by a number of hash functions, and all the elements of the sets share a single bit string. When querying, simply use the same number of hash functions to process the data to be queried, and directly access all the corresponding bits to see if they are activated to 1. If all of them are 1, it means that the data exists. Otherwise, it does not exist. The probability of collision can be achieved by controlling the number of hash functions. The communication overhead of the latter is lower compared to sending the entire set and sending a single bit string from the output of the Bloom filter. The computation can also be accelerated by parallelism during the creation of the bloom filter and the use of the filter for large-scale data queries.
-
-## Quick Experience
-
-### Front-end Needs
-
-Finish installing the `mindspore-federated` library in the Python environment.
-
-### Starting the Script
-
-You can get the PSI start script for both sides from [MindSpore federated ST](https://gitee.com/mindspore/federated/blob/master/tests/st/psi/run_psi.py) and open two processes to simulate both sides. The start command of local device and local communication:
-
-```python
-python run_psi.py --comm_role="server" --http_server_address="127.0.0.1:8004" --remote_server_address="127.0.0.1:8005" --input_begin=1 --input_end=100
-
-python run_psi.py --comm_role="client" --http_server_address="127.0.0.1:8005" --remote_server_address="127.0.0.1:8004" --input_begin=50 --input_end=150
-```
-
-- `input_begin` is used in conjunction with `input_end` to generate the dataset for intersection.
-- `peer_input_begin` and `peer_input_end` indicate the start and end ranges of each other's data, taking `--need_check` as `True`, which can be intersected by the Python set1.intersection(set2) function to get the true result, and is used to check the correctness of the PSI.
-- `---bucket_size` (optional) indicates the number of for loops that serially perform multiple bucket intersections.
-- `--thread_num` (optional) indicates the number of parallel threads used for the calculation.
-- To run plaintext intersection, add the parameter `--plain_intersection=True` to the command.
-
-At present, psi supports the intersection of hundreds of millions of large data. You can specify the size of the input data set by setting 'input_begin', 'input_end', 'peer_input_begin', and 'peer_input_end' parameters. The theory proves that the memory resources of the machine and the system are sufficient, and there is no upper limit to the number of data computations psi can support. The startup command is as follows:
-
-```python
-python run_psi.py --comm_role="server" --http_server_address="127.0.0.1:8004" --remote_server_address="127.0.0.1:8005" --input_begin=1 --input_end=100000000
-
-python run_psi.py --comm_role="client" --http_server_address="127.0.0.1:8005" --remote_server_address="127.0.0.1:8004" --input_begin=1 --input_end=100000000
-```
-
-### Output Results
-
-Before running the script, you can set the environment variable `export GLOG_v=1` to display the `INFO` level log, and also observe the operation of each phase within the protocol. After running the script, the intersection results will be printed out. As the amount of intersection data may be too large, the output here is limited to the first 20 intersection results.
-
-```bash
-PSI result: ['50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69'] (display limit: 20)
-```
-
-## Deep Experience
-
-### Import Module
-
-To run the privacy set intersection, you need to rely on the communication module and the intersection module of the Federated Library, which are imported as follows:
-
-```python
-from mindspore_federated.startup.vertical_federated_local import VerticalFederatedCommunicator, ServerConfig
-from mindspore_federated._mindspore_federated import RunPSI
-from mindspore_federated._mindspore_federated import PlainIntersection
-```
-
-### Data Preparation
-
-Both `RunPSI` and `PlainIntersection` require input data in `List(String)` format, and methods for generating datasets via file reading and for loops are given here:
-
-```python
-def generate_input_data(input_begin_, input_end_, read_file_, file_name_):
-    input_data_ = []
-    if read_file_:
-        with open(file_name_, 'r') as f:
-            for line in f.readlines():
-                input_data_.append(line.strip())
-    else:
-        input_data_ = [str(i) for i in range(input_begin_, input_end_)]
-    return input_data_
-```
-
-The input parameters `input_begin_` and `input_end_` limit the data range of the for loop. `read_file_` and `file_name_` indicate whether to read the file and the path where the file is located. The file can be constructed by itself, each line representing one piece of data.
-
-### Constructing Communication
-
-Before calling this interface, a vertical federated communication instance needs to be initialized, as follows:
-
-```python
-http_server_config = ServerConfig(server_name=comm_role, server_address=http_server_address)
-remote_server_config = ServerConfig(server_name=peer_comm_role, server_address=remote_server_address)
-vertical_communicator = VerticalFederatedCommunicator(http_server_config=http_server_config,
-                                                      remote_server_config=remote_server_config)
-vertical_communicator.launch()
-```
-
-- `server_name` is determined by whether the process belongs to `server` or `client`. `comm_role` is assigned to the corresponding `server` or `client`, and `peer_comm_role_` indicates the role of the other party.
-- The format of `server_address` is "IP:port". `http_server_address` is assigned to the `IP` and `port` information of the process, such as "127.0.0.1:8004". `remote_server_address` is assigned to the `IP` and `port` information of the other party.
-
-### Starting Intersection
-
-The external interfaces for secure set intersection are `RunPSI` and `PlainIntersection`, which are ciphertext and plaintext intersections respectively, with the same type and meaning of input and return results. Only ciphertext intersection `RunPSI` is described here:
-
-```python
-result = RunPSI(input_data, comm_role, peer_comm_role, bucket_id, thread_num)
-```
-
-- `input_data`: (list[string]), psi, the input data of one party.
-- `comm_role`: (string), communication-related parameter, "server" or "client".
-- `peer_comm_role`: (string), communication-related parameter, "server" or "client", different with comm_role.
-- `bucket_id`: (int), outer part of the barrel, serial number of the pass-in barrel. `TypeError` error for passing in negative numbers, decimals or other types. If the value is different between two processes, the server will exit with an error and the client will block and wait.
-- `thread_num`: (int), number of threads, natural number. 0 is the default value, which means use the maximum number of threads available on the machine minus 5, and other values will be limited to 1 to the maximum available on the machine. `TypeError` error for passing in negative numbers, decimals or other types.
-
-### Output Results
-
-The `result` is in `list[string]` format, which represents the intersection result and can be printed out by itself. Here's the method of the Python set intersection:
-
-```python
-def compute_right_result(self_input, peer_input):
-    self_input_set = set(self_input)
-    peer_input_set = set(peer_input)
-    return self_input_set.intersection(peer_input_set)
-```
-
-The results of the above methods can be compared with `result` to check if they are consistent, and the correctness of the interface can be verified.
diff --git a/docs/federated/docs/source_en/secure_vertical_federated_learning_with_DP.md b/docs/federated/docs/source_en/secure_vertical_federated_learning_with_DP.md
deleted file mode 100644
index 6a30234678..0000000000
--- a/docs/federated/docs/source_en/secure_vertical_federated_learning_with_DP.md
+++ /dev/null
@@ -1,158 +0,0 @@
-# Vertical Federated - Label Protection Based on Differential Privacy
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/secure_vertical_federated_learning_with_DP.md)
-
-## Background
-
-Vertical federated learning (vFL) is a major branch of federated learning (FL). When different participants have data from the same batch of users but with different attributes, they can use vFL for collaborative training. In vFL, the participants with user features (follower for short, participant A as shown in the figure below) hold a bottom network (Bottom Model). They input the features into the bottom network, compute the intermediate results (embedding), and send them to the participants with labels (leader for short, participant B as shown in the figure below). The leader uses these embeddings and its own labels to train the upper network (upper network), and then passes the computed gradients back to each participant to train the bottom network. It can be seen that vFL does not require any participant to upload their own raw data to collaboratively train the model.
-
-![image.png](./images/vfl_1_en.png)
-
-The vFL framework avoids the direct upload of raw data, protecting data privacy at a certain level. However, there exists possibility for a semi-honest or a malicious party to infer the label information from the gradients passing back from the leader party, causing privacy disclosure. Given the large number of vFL scenarios where labels are the most valuable and most important piece of information to protect, in this context, we need to provide stronger privacy guarantees for vFL training to avoid the privacy disclosure.
-
-Differential privacy (DP) is a definition of privacy based strictly on statistics/information theory, which currently the golden standard of privacy-preserving data analysis. The core idea behind DP is to induce randomness to overwhelm each individual data's influence on the algorithm's result, making sure that it is hard for the algorithm's results to be inverted to the individual data. The protection of DP can hold under an extreme threat model, which holds even when:
-
-- the adversary knows all the details of the DP algorithm
-- the adversary has infinite computing power
-- the adversary has arbitrary auxiliary information about the raw data
-
-Regarding the backgrounds, theories and implementation of DP, please refer to [1] for an excellent survey.
-
-Our scheme is based on label differential privacy (label dp) [2], which provides differential privacy guarantees for the labels of the leader participants during vertical federated learning training, so that an attacker cannot invert the label information of the data from the returned gradients. Under the protection of this scheme, even if the follower party is semi-honest or malicious, the label information of the leader party is guaranteed to be protected, mitigating vFL participants' concerns on the data privacy risk.
-
-## Algorithm Implementation
-
-MindSpore Federated adopt a lightweight implementation of label dp. During training, a certain percentage of the labels are randomly flipped before using the label data from the leader participants. Due to the introduction of randomness, an attacker who wants to invert the labels can at most invert the labels after the random flip or perturbation, increasing the difficulty of inverting the original labels and satisfying the differential privacy guarantee. In practical applications, we can adjust the privacy parameter `eps` (which can be interpreted as the ratio of randomly flipped labels) to meet the needs of different scenarios:
-
-- smaller `eps` (<1.0) corresponds to high privacy, low performance
-- larger `eps` (>5.0) corresponds to high performance, low privacy
-
-![image.png](./images/label_dp_en.png)
-
-The implementation of this scheme is divided into the binary case and the onehot case. Whether the input labels are binary or onehot is automatically recognized, and the same type of labels will then be output. The detailed algorithm is shown as follows:
-
-### Binary Labels Protection
-
-1. Calculate the flip probability $p = \frac{1}{1 + e^{eps}}$ according to the preset privacy parameter eps.
-2. Flip each label with probability $p$.
-
-### Onehot Labels Protection
-
-1. For n classes of labels, calculate $p_1 = \frac{e^{eps}}{n - 1 + e^{eps}}$, $p_2 = \frac{1}{n - 1 + e^{eps}}$.
-2. Randomly scramble the labels according to the following probabilities: the probability of keeping the current label unchanged is $p_1$ and the probability of changing to any of the other n - 1 classes is $p_2$.
-
-## Quick Experience
-
-We use the local case in [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo) as an example of how to add label dp to a vertical federated model protection.
-
-### Front-End Needs
-
-1. Install MindSpore 1.8.1 or its higher version, please refer to [MindSpore official website installation guide](https://www.mindspore.cn/install).
-2. Install MindSpore Federated and the Python libraries which the MindSpore Federated depends on.
-
-   ```shell
-   cd federated
-   python -m pip install -r requirements_test.txt
-   ```
-
-3. Prepare the criteo dataset, please refer to [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo).
-
-### Starting the Script
-
-1. Download federated
-
-   ```bash
-   git clone https://gitee.com/mindspore/federated.git
-   ```
-
-2. Go to the folder where the script is located
-
-   ```bash
-   cd federated/example/splitnn_criteo
-   ```
-
-3. Run the script
-
-   ```bash
-   sh run_vfl_train_local_label_dp.sh
-   ```
-
-### Viewing Results
-
-Check loss changes of the model training in the training log `log_local_gpu.txt`.
-
-```sh
-INFO:root:epoch 0 step 100/2582 loss: 0.588637
-INFO:root:epoch 0 step 200/2582 loss: 0.561055
-INFO:root:epoch 0 step 300/2582 loss: 0.556246
-INFO:root:epoch 0 step 400/2582 loss: 0.557931
-INFO:root:epoch 0 step 500/2582 loss: 0.553283
-INFO:root:epoch 0 step 600/2582 loss: 0.549618
-INFO:root:epoch 0 step 700/2582 loss: 0.550243
-INFO:root:epoch 0 step 800/2582 loss: 0.549496
-INFO:root:epoch 0 step 900/2582 loss: 0.549224
-INFO:root:epoch 0 step 1000/2582 loss: 0.547547
-INFO:root:epoch 0 step 1100/2582 loss: 0.546989
-INFO:root:epoch 0 step 1200/2582 loss: 0.552165
-INFO:root:epoch 0 step 1300/2582 loss: 0.546926
-INFO:root:epoch 0 step 1400/2582 loss: 0.558071
-INFO:root:epoch 0 step 1500/2582 loss: 0.548258
-INFO:root:epoch 0 step 1600/2582 loss: 0.546442
-INFO:root:epoch 0 step 1700/2582 loss: 0.549062
-INFO:root:epoch 0 step 1800/2582 loss: 0.546558
-INFO:root:epoch 0 step 1900/2582 loss: 0.542755
-INFO:root:epoch 0 step 2000/2582 loss: 0.543118
-INFO:root:epoch 0 step 2100/2582 loss: 0.542587
-INFO:root:epoch 0 step 2200/2582 loss: 0.545770
-INFO:root:epoch 0 step 2300/2582 loss: 0.554520
-INFO:root:epoch 0 step 2400/2582 loss: 0.551129
-INFO:root:epoch 0 step 2500/2582 loss: 0.545622
-...
-```
-
-## Deep Experience
-
-We take the local case in [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo) as an example to introduce the specific operation method of adding label dp protection in the vertical federated model.
-
-### Front-End Needs
-
-Same as [Quick Experience](#quick-experience): Install MindSpore, Install MindSpore Federated, and Prepare dataset.
-
-### Option 1: Call the integrated label dp function in the FLModel class
-
-MindSpore Federated uses `FLModel` (see [Vertical Federated Learning Model Training Interface](https://www.mindspore.cn/federated/docs/en/master/vertical/vertical_federated_FLModel.html)) and yaml files (see [detailed configuration items of Vertical Federated Learning yaml](https://www.mindspore.cn/federated/docs/en/master/vertical/vertical_federated_yaml.html)) to model the training process of vertical federated learning.
-
-We have integrated the label dp function in the `FLModel` class. After the normal completion of modeling the entire vertical federated learning training process (for detailed vFl training, see [Vertical Federated Learning Model Training - Pangu Alpha Large Model Cross-Domain Training](https://www.mindspore.cn/federated/docs/en/master/split_pangu_alpha_application.html)), users can simply add the `label_dp` submodule under the `privacy` module in the yaml file of the label side (or add it by user if there is no `privacy` module), and set the `eps` parameter in the `label_dp` module (differential privacy parameter $\epsilon$, the user can set the value of this parameter according to the actual needs). Let the model enjoy label dp protection:
-
-```yaml
-privacy:
-  label_dp:
-    eps: 1.0
-```
-
-### Option 2: Directly call the LabelDP class
-
-Users can also call the `LabelDP` class directly to use the label dp function more flexibly. The `LabelDP` class is integrated in the `mindspore_federated.privacy` module. The user can define a `LabelDP` object by specifying the value of `eps`, and then pass the label group as an argument to this object. The `_call_` functio of objext will automatically recognize whether the current input is one-hot or binary label and outputs a label group processed by label dp. Refer to the following example:
-
-```python
-# make private a batch of binary labels
-import numpy as np
-import mindspore
-from mindspore import Tensor
-from mindspore_federated.privacy import LabelDP
-label_dp = LabelDP(eps=0.0)
-label = Tensor(np.zero(5, 1), dtype=mindspore.float32)
-dp_label = label_dp(label)
-
-# make private a batch of one-hot labels
-label = Tensor(np.hstack((np.ones((5, 1)), np.zeros((5, 2)))), dtype=mindspore.float32)
-dp_label = label_dp(label)
-print(dp_label)
-```
-
-## References
-
-[1] Dwork C, Roth A. The algorithmic foundations of differential privacy[J]. Foundations and Trends® in Theoretical Computer Science, 2014, 9(3–4): 211-407.
-
-[2] Ghazi B, Golowich N, Kumar R, et al. Deep learning with label differential privacy[J]. Advances in Neural Information Processing Systems, 2021, 34: 27131-27145.
-
diff --git a/docs/federated/docs/source_en/secure_vertical_federated_learning_with_EmbeddingDP.md b/docs/federated/docs/source_en/secure_vertical_federated_learning_with_EmbeddingDP.md
deleted file mode 100644
index 6b1e2523ff..0000000000
--- a/docs/federated/docs/source_en/secure_vertical_federated_learning_with_EmbeddingDP.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# Vertical Federated-Feature Protection Based on Information Obfuscation
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/secure_vertical_federated_learning_with_EmbeddingDP.md)
-
-## Background
-
-Vertical Federated Learning (vFL) is a mainstream and important joint learning paradigm. In vFL, n (n ≥ 2) participants have a large number of identical users, but the overlap of user characteristics is small. MindSpore Federated uses Split Learning (SL) technology to implement vFL. Taking the two-party split learning shown in the figure below as an example, each participant does not share the original data directly, but shares the intermediate features extracted by the local model for training and inference, satisfying the privacy requirement that the original data will not be leaked.
-
-However, it has been shown [1] that an attacker (e.g., participant 2) can reduce the corresponding original data (feature) by intermediate features (E), resulting in privacy leakage. For such feature reconstruction attacks, this tutorial provides a lightweight feature protection scheme based on information obfuscation [2].
-
-![image.png](./images/vfl_feature_reconstruction_en.png)
-
-## Scheme Details
-
-The protection scheme is named EmbeddingDP, and the overall picture is shown below. For the generated intermediate features E, the obfuscation operations such as Quantization and Differential Privacy (DP) are applied sequentially to generate P and send P to the participant 2 as an intermediate feature. The obfuscation operation greatly reduces the correlation between the intermediate features and the original input, which makes the attack more difficult.
-
-![image.png](./images/vfl_feature_reconstruction_defense_en.png)
-
-Currently, this tutorial supports single-bit quantization and differential privacy protection based on random responses, and the details of the scheme are shown in the figure below.
-
-1. **Single-bit quantization**: For the input vector E, single-bit quantization will set the number greater than 0 to 1 and the number less than or equal to 0 to 0, generating the binary vector B.
-
-2. **Differential privacy based on random responses (DP)**: Differential privacy requires the configuration of the key parameter `eps`. If `eps` is not configured, no differential privacy is performed and the binary vector B is directly used as the intermediate feature to be transmitted. If `eps` is correctly configured (i.e., `eps` is a non-negative real number), the larger `eps` is, the lower the probability of confusion and the smaller the impact on the data, and at the same time, the privacy protection is relatively weak. For any dimension i in the binary vector B, if B[i] = 1, the value is kept constant with probability p. If B[i] = 0, B[i] is flipped with probability q, i.e., so that B[i] = 1. Probabilities p and q are calculated based on the following equations, where e denotes the natural base number.
-
-$$p = \frac{e^{(eps / 2)}}{e^{(eps / 2)} + 1},\quad q = \frac{1}{e^{(eps / 2)} + 1}$$
-
-![image.png](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/vfl_mnist_detail.png)
-
-## Feature Experience
-
-This feature can work with one-dimensional or two-dimensional tensor arrays. One-dimensional arrays can only consist of the numbers 0 and 1, and two-dimensional arrays need to consist of one-dimensional vectors in the one-hot encoded format. After [installing MindSpore and Federated](https://mindspore.cn/federated/docs/en/master/federated_install.html#obtaining-mindspore-federated), this feature can be applied to process a tensor array that meets the requirements, as shown in the following sample program:
-
-```python
-import mindspore as ms
-from mindspore import Tensor
-from mindspore.common.initializer import Normal
-from mindspore_federated.privacy import EmbeddingDP
-
-ori_tensor = Tensor(shape=(2,3), dtype=ms.float32, init=Normal())
-print(ori_tensor)
-dp_tensor = EmbeddingDP(eps=1)(ori_tensor)
-print(dp_tensor)
-```
-
-## Application Examples
-
-### Protecting the Pangu Alpha Large Model Cross-Domain Training
-
-#### Preparation
-
-Download the federated code repository and follow the tutorial [Longitudinal Federated Learning Model Training - Pangu Alpha Large Model Cross-Domain Training](https://mindspore.cn/federated/docs/en/master/split_pangu_alpha_application.html#environment-preparation), configure the runtime environment and experimental dataset, and then run the single-process or multi-process example program as needed.
-
-```bash
-git clone https://gitee.com/mindspore/federated.git
-```
-
-#### Single-process Sample
-
-1. Go to the directory where the sample is located and execute [running single-process sample](https://mindspore.cn/federated/docs/en/master/split_pangu_alpha_application.html#running-a-single-process-example) in steps 2 to 4:
-
-    ```bash
-    cd federated/example/splitnn_pangu_alpha
-    ```
-
-2. Start the training script with EmbeddingDP configured:
-
-    ```bash
-    sh run_pangu_train_local_embedding_dp.sh
-    ```
-
-3. View the training loss in the training log `splitnn_pangu_local.txt`:
-
-    ```text
-    2023-02-07 01:34:00 INFO: The embedding is protected by EmbeddingDP with eps 5.000000.
-    2023-02-07 01:35:40 INFO: epoch 0 step 10/43391 loss: 10.653997
-    2023-02-07 01:36:25 INFO: epoch 0 step 20/43391 loss: 10.570406
-    2023-02-07 01:37:11 INFO: epoch 0 step 30/43391 loss: 10.470503
-    2023-02-07 01:37:58 INFO: epoch 0 step 40/43391 loss: 10.242296
-    2023-02-07 01:38:45 INFO: epoch 0 step 50/43391 loss: 9.970814
-    2023-02-07 01:39:31 INFO: epoch 0 step 60/43391 loss: 9.735226
-    2023-02-07 01:40:16 INFO: epoch 0 step 70/43391 loss: 9.594692
-    2023-02-07 01:41:01 INFO: epoch 0 step 80/43391 loss: 9.340107
-    2023-02-07 01:41:47 INFO: epoch 0 step 90/43391 loss: 9.356388
-    2023-02-07 01:42:34 INFO: epoch 0 step 100/43391 loss: 8.797981
-    ...
-    ```
-
-#### Multi-process Sample
-
-1. Go to the directory where the sample is located, install the dependency packages, and configure the dataset:
-
-    ```bash
-    cd federated/example/splitnn_pangu_alpha
-    python -m pip install -r requirements.txt
-    cp -r {dataset_dir}/wiki ./
-    ```
-
-2. Start the training script on Server 1 with EmbeddingDP configured:
-
-    ```bash
-    sh run_pangu_train_leader_embedding_dp.sh {ip1:port1} {ip2:port2} ./wiki/train ./wiki/train
-    ```
-
-    `ip1` and `port1` denote the IP address and port number of the participating local server (server 1). `ip2` and `port2` denote the IP address and port number of the peer server (server 2). `./wiki/train` is the training dataset file path, and `./wiki/test` is the evaluation dataset file path.
-
-3. Start training script of another participant on Server 2:
-
-    ```bash
-    sh run_pangu_train_follower.sh {ip2:port2} {ip1:port1}
-    ```
-
-4. View the training loss in the training log `leader_process.log`:
-
-    ```text
-    2023-02-07 01:39:15 INFO: config is:
-    2023-02-07 01:39:15 INFO: Namespace(ckpt_name_prefix='pangu', ...)
-    2023-02-07 01:39:21 INFO: The embedding is protected by EmbeddingDP with eps 5.000000.
-    2023-02-07 01:41:05 INFO: epoch 0 step 10/43391 loss: 10.669225
-    2023-02-07 01:41:38 INFO: epoch 0 step 20/43391 loss: 10.571924
-    2023-02-07 01:42:11 INFO: epoch 0 step 30/43391 loss: 10.440327
-    2023-02-07 01:42:44 INFO: epoch 0 step 40/43391 loss: 10.253876
-    2023-02-07 01:43:16 INFO: epoch 0 step 50/43391 loss: 9.958257
-    2023-02-07 01:43:49 INFO: epoch 0 step 60/43391 loss: 9.704673
-    2023-02-07 01:44:21 INFO: epoch 0 step 70/43391 loss: 9.543740
-    2023-02-07 01:44:54 INFO: epoch 0 step 80/43391 loss: 9.376131
-    2023-02-07 01:45:26 INFO: epoch 0 step 90/43391 loss: 9.376905
-    2023-02-07 01:45:58 INFO: epoch 0 step 100/43391 loss: 8.766671
-    ...
-    ```
-
-## Works Cited
-
-[1] Erdogan, Ege, Alptekin Kupcu, and A. Ercument Cicek. "Unsplit: Data-oblivious model inversion, model stealing, and label inference attacks against split learning." arXiv preprint arXiv:2108.09033 (2021).
-
-[2] Anonymous Author(s). "MistNet: Towards Private Neural Network Training with Local Differential Privacy". (https://github.com/TL-System/plato/blob/2e5290c1f3acf4f604dad223b62e801bbefea211/docs/papers/MistNet.pdf)
diff --git a/docs/federated/docs/source_en/secure_vertical_federated_learning_with_TEE.md b/docs/federated/docs/source_en/secure_vertical_federated_learning_with_TEE.md
deleted file mode 100644
index a2ba1b5df7..0000000000
--- a/docs/federated/docs/source_en/secure_vertical_federated_learning_with_TEE.md
+++ /dev/null
@@ -1,281 +0,0 @@
-# Vertical Federated - Feature Protection Based on Trusted Execution Environment
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/secure_vertical_federated_learning_with_TEE.md)
-
-Note: This is an experimental feature and may be modified or removed in the future.
-
-## Background
-
-Vertical federated learning (vFL) is a major branch of federated learning (FL). When different participants have data from the same batch of users but with different attributes, they can use vFL for collaborative training. In vFL, each participant with attributes holds a bottom model, and they input the attributes into the bottom model to get the intermediate result (embedding), which is sent to the participant with labels (referred to as leader paraticipant, participant B as shown in the figure below, as shown in the figure below, and the participant without labels, called follower, as shown in the figure below, as participant A). The leader side uses the embedding and labels to train the upper layer network, and then passes the calculated gradients back to each participant to train the lower layer network. It can be seen that vFL does not require any participant to upload their own raw data to collaboratively train the model.
-
-![image.png](./images/vfl_1_en.png)
-
-By avoiding direct uploading of raw data, vFL protects privacy security to a certain extent, which is one of the core goals of vFL. However, it is still possible for an attacker to reverse user information from the uploaded embedding, causing privacy security risks. In such a context, we need to provide stronger privacy guarantees for the embedding and gradients transmitted during vFL training to circumvent privacy security risks.
-
-Trusted execution environment (TEE) is a hardware-based trusted computing solution that provides data security of the computing process by making the whole computing process in hardware black-boxed relative to the outside world. By shielding the key layer in the vFL network through TEE, it can make the computation of that layer difficult to be reversed, thus ensuring the data security of the vFL training and inference process.
-
-## Algorithm Introduction
-
-![image.png](./images/vfl_with_tee_en.png)
-
-As shown in the figure, if participant A sends the intermediate result $\alpha^{(A)}$ directly to participant B, it is easy for participant B to use the intermediate result to reverse the original data $X^{(A)}$ of participant A. To reduce such risk, participant A encrypts the intermediate result $\alpha^{(A)}$ computed by Bottom Model to get $E(\alpha^{(A)})$ first, and passes $E(\alpha^{(A)})$ to participant B. Participant B inputs $E(\alpha^{(A)})$ into the TEE-based Cut Layer, and then decrypts it into $\alpha^{(A)}$ for forward propagation inside the TEE, and the whole process is black-boxed for B.
-
-The gradient is passed backward similarly, Cut Layer computes the gradient $\nabla\alpha^{(A)}$, encrypts it into $E(\nabla\alpha^{(A)})$ and then passes it back from participant B to participant A. Then participant A decrypts it into $\nabla\alpha^{(A)}$ and continues to do backward propagation.
-
-## Quick Experience
-
-We use the local case in [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo) as an example of configuring TEE protection.
-
-### Front-End Needs and Environment Configuration
-
-1. Environmental requirements.
-
-   - Processor: Intel SGX (Intel Sofrware Guard Extensions) support required
-   - OS: openEuler 20.03, openEuler 21.03 LTS SP2 or higher
-
-2. Install SGX and SecGear (you can refer to [secGear official website](https://gitee.com/openeuler/secGear)).
-
-   ```sh
-   sudo yum install -y cmake ocaml-dune linux-sgx-driver sgxsdk libsgx-launch libsgx-urts sgxssl
-   git clone https://gitee.com/openeuler/secGear.git
-   cd secGear
-   source /opt/intel/sgxsdk/environment && source environment
-   mkdir debug && cd debug && cmake .. && make && sudo make install
-   ```
-
-3. Install MindSpore 1.8.1 or its higher version, please refer to the [MindSpore Official Site Installation Guide](https://www.mindspore.cn/install).
-
-4. Download federated
-
-   ```sh
-   git clone https://gitee.com/mindspore/federated.git
-   ```
-
-5. Download four lib files as TEE dependencies: [libsgx_0.so](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/tutorials-develop/federated/libsgx_0.so), [libsecgear.so](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/tutorials-develop/federated/libsecgear.so), [enclave.signed.so](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/tutorials-develop/federated/enclave.signed.so) and [libcsecure_channel_static.a](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/tutorials-develop/federated/libcsecure_channel_static.a). Put them into `mindspore_federated/fl_arch/ccsrc/armour/lib` (make new directory required).
-
-6. For installing MindSpore Federated relies on Python libraries, see [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo).
-
-7. Install MindSpore Federated for TEE compilation (need to additionally set compiler options to indicate whether to use SGX or not).
-
-   ```sh
-   sh federated/build.sh -s on
-   pip install federated/build/packages/mindspore_federated-XXXXX.whl
-   ```
-
-8. To prepare the criteo dataset, please refer to [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo).
-
-### Starting the Script
-
-1. Go to the folder where the script is located
-
-   ```sh
-   cd federated/example/splitnn_criteo
-   ```
-
-2. Run the script
-
-   ```sh
-   sh run_vfl_train_local_tee.sh
-   ```
-
-### Viewing Results
-
-Check loss changes of the model training in the training log `log_local_cpu_tee.txt`.
-
-```sh
-INFO:root:epoch 0 step 100/41322 wide_loss: 0.661822 deep_loss: 0.662018
-INFO:root:epoch 0 step 100/41322 wide_loss: 0.685003 deep_loss: 0.685198
-INFO:root:epoch 0 step 200/41322 wide_loss: 0.649380 deep_loss: 0.649381
-INFO:root:epoch 0 step 300/41322 wide_loss: 0.612189 deep_loss: 0.612189
-INFO:root:epoch 0 step 400/41322 wide_loss: 0.630079 deep_loss: 0.630079
-INFO:root:epoch 0 step 500/41322 wide_loss: 0.602897 deep_loss: 0.602897
-INFO:root:epoch 0 step 600/41322 wide_loss: 0.621647 deep_loss: 0.621647
-INFO:root:epoch 0 step 700/41322 wide_loss: 0.624762 deep_loss: 0.624762
-INFO:root:epoch 0 step 800/41322 wide_loss: 0.622042 deep_loss: 0.622042
-INFO:root:epoch 0 step 900/41322 wide_loss: 0.585274 deep_loss: 0.585274
-INFO:root:epoch 0 step 1000/41322 wide_loss: 0.590947 deep_loss: 0.590947
-INFO:root:epoch 0 step 1100/41322 wide_loss: 0.586775 deep_loss: 0.586775
-INFO:root:epoch 0 step 1200/41322 wide_loss: 0.597362 deep_loss: 0.597362
-INFO:root:epoch 0 step 1300/41322 wide_loss: 0.607390 deep_loss: 0.607390
-INFO:root:epoch 0 step 1400/41322 wide_loss: 0.584204 deep_loss: 0.584204
-INFO:root:epoch 0 step 1500/41322 wide_loss: 0.583618 deep_loss: 0.583618
-INFO:root:epoch 0 step 1600/41322 wide_loss: 0.573294 deep_loss: 0.573294
-INFO:root:epoch 0 step 1700/41322 wide_loss: 0.600686 deep_loss: 0.600686
-INFO:root:epoch 0 step 1800/41322 wide_loss: 0.585533 deep_loss: 0.585533
-INFO:root:epoch 0 step 1900/41322 wide_loss: 0.583466 deep_loss: 0.583466
-INFO:root:epoch 0 step 2000/41322 wide_loss: 0.560188 deep_loss: 0.560188
-INFO:root:epoch 0 step 2100/41322 wide_loss: 0.569232 deep_loss: 0.569232
-INFO:root:epoch 0 step 2200/41322 wide_loss: 0.591643 deep_loss: 0.591643
-INFO:root:epoch 0 step 2300/41322 wide_loss: 0.572473 deep_loss: 0.572473
-INFO:root:epoch 0 step 2400/41322 wide_loss: 0.582825 deep_loss: 0.582825
-INFO:root:epoch 0 step 2500/41322 wide_loss: 0.567196 deep_loss: 0.567196
-INFO:root:epoch 0 step 2600/41322 wide_loss: 0.602022 deep_loss: 0.602022
-```
-
-## Deep Experience
-
-The forward and backward propagation of the TEE layer requires calling its own functions rather than MindSpore, so there are differences in implementation from the usual vFL model.
-
-Usually, the Top Model and Cut Layer are put together for the backpropagation of the vFL model during training, and are derived and updated in one step by Participant B through MindSpore. When the network containing TEE is back propagated, the Top Model is updated by Participant B based on MindSpore, while the Cut Layer (TEE) is updated within itself after receiving the gradients back from the Top Model. The gradients that need to be passed back to Participant A are encrypted and passed out to Participant B. The whole process is done within the TEE.
-
-Currently in MindSpore Federated, the above function is used to implement a custom backward propagation process by passing `grad_network` into the `mindspore_federated.vfl_model.FLModel()` definition. Therefore, to implement a network containing TEE, the user can define the backward propagation process for Top Model and Cut Layer in `grad_network` and just pass in `FLModel`, and `FLModel` will go through the user-defined training process during backward propagation.
-
-We use the local case in [Wide&Deep Vertical Federated Learning Case](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo) as an example of how to configure TEE protection in a vertical federated model. The presentation focuses on the differences between the configuration and the usual case when using TEE, and the same points will be skipped (a detailed description of vFL training can be found in [Vertical Federated Learning Model Training - Pangu Alpha Large Model Cross-Domain Training](https://mindspore.cn/federated/docs/en/master/split_pangu_alpha_application.html)).
-
-### Front-End Needs and Environment Configuration
-
-Refer to [Quick Experience](#quick-experience).
-
-### Defining the Network Model
-
-#### Forward Propagation
-
-As usual vFL training, users need to define a network model containing TEE based on the `nn.Cell` provided by MindSpore (see [mindspore.nn.Cell](https://mindspore.cn/docs/en/master/api_python/nn/mindspore.nn.Cell.html#mindspore-nn-cell)) to develop the training network. The difference is that at the layer where the TEE is located, the user needs to call the TEE forward propagation function in the `construct` function of the class:
-
-```python
-from mindspore_federated._mindspore_federated import init_tee_cut_layer, backward_tee_cut_layer, \
-    encrypt_client_data, secure_forward_tee_cut_layer
-
-class TeeLayer(nn.Cell):
-    """
-    TEE layer of the leader net.
-    Args:
-        config (class): default config info.
-    """
-    def __init__(self, config):
-        super(TeeLayer, self).__init__()
-        init_tee_cut_layer(config.batch_size, 2, 2, 1, 3.5e-4, 1024.0)
-        self.concat = ops.Concat(axis=1)
-        self.reshape = ops.Reshape()
-
-    def construct(self, wide_out0, deep_out0, wide_embedding, deep_embedding):
-        """Convert and encrypt the intermediate data"""
-        local_emb = self.concat((wide_out0, deep_out0))
-        remote_emb = self.concat((wide_embedding, deep_embedding))
-        aa = remote_emb.flatten().asnumpy().tolist()
-        bb = local_emb.flatten().asnumpy().tolist()
-        enc_aa, enc_aa_len = encrypt_client_data(aa, len(aa))
-        enc_bb, enc_bb_len = encrypt_client_data(bb, len(bb))
-        tee_output = secure_forward_tee_cut_layer(remote_emb.shape[0], remote_emb.shape[1],
-                                                  local_emb.shape[1], enc_aa, enc_aa_len, enc_bb, enc_bb_len, 2)
-        tee_output = self.reshape(Tensor(tee_output), (remote_emb.shape[0], 2))
-        return tee_output
-```
-
-#### Backward Propagation
-
-In the usual vfl model, backward propagation is automatically configured by the `FLModel` class, but in models containing TEE, the user needs to develop a `grad_network` to define the backward propagation process. `grad_network` is also based on `nn.Cell` and includes a `__init__` function and a `construct` function. When initializing, you need to pass in the network used for training and define in the `__init__` function: the derivative operator, the parameters for the network outside Cut Layer, the loss function, the Optimizer for the network outside the Cut Layer. The example is as follows:
-
-```python
-class LeaderGradNet(nn.Cell):
-    """
-    grad_network of the leader party.
-    Args:
-        net (class): LeaderNet, which is the net of leader party.
-        config (class): default config info.
-    """
-
-    def __init__(self, net: LeaderNet):
-        super().__init__()
-        self.net = net
-        self.sens = 1024.0
-
-        self.grad_op_param_sens = ops.GradOperation(get_by_list=True, sens_param=True)
-        self.grad_op_input_sens = ops.GradOperation(get_all=True, sens_param=True)
-
-        self.params_head = ParameterTuple(net.head_layer.trainable_params())
-        self.params_bottom_deep = vfl_utils.get_params_by_name(self.net.bottom_net, ['deep', 'dense'])
-        self.params_bottom_wide = vfl_utils.get_params_by_name(self.net.bottom_net, ['wide'])
-
-        self.loss_net = HeadLossNet(net.head_layer)
-        self.loss_net_l2 = L2LossNet(net.bottom_net, config)
-
-        self.optimizer_head = Adam(self.params_head, learning_rate=3.5e-4, eps=1e-8, loss_scale=self.sens)
-        self.optimizer_bottom_deep = Adam(self.params_bottom_deep, learning_rate=3.5e-4, eps=1e-8, loss_scale=self.sens)
-        self.optimizer_bottom_wide = FTRL(self.params_bottom_wide, learning_rate=5e-2, l1=1e-8, l2=1e-8,
-                                          initial_accum=1.0, loss_scale=self.sens)
-```
-
-The input to the `construct` function of `grad_network` is two dictionaries `local_data_batch` and `remote_data_batch`. In the `construct` function, you first need to extract the corresponding data from the dictionaries. Next, the layers other than TEE need to call the MindSpore derivative operators on the parameters and the input for the derivative operation and update with the optimizer respectively. The TEE layer needs to call the built-in functions of TEE for the derivative and update. The examples are as follows:
-
-```python
-def construct(self, local_data_batch, remote_data_batch):
-    """
-    The backward propagation of the leader net.
-    """
-    # data processing
-    id_hldr = local_data_batch['id_hldr']
-    wt_hldr = local_data_batch['wt_hldr']
-    label = local_data_batch['label']
-    wide_embedding = remote_data_batch['wide_embedding']
-    deep_embedding = remote_data_batch['deep_embedding']
-
-    # forward
-    wide_out0, deep_out0 = self.net.bottom_net(id_hldr, wt_hldr)
-    local_emb = self.concat((wide_out0, deep_out0))
-    remote_emb = self.concat((wide_embedding, deep_embedding))
-    head_input = self.net.cut_layer(wide_out0, deep_out0, wide_embedding, deep_embedding)
-    loss = self.loss_net(head_input, label)
-
-    # update of head net
-    sens = ops.Fill()(ops.DType()(loss), ops.Shape()(loss), 1024.0)
-    grad_head_input, _ = self.grad_op_input_sens(self.loss_net)(head_input, label, sens)
-    grad_head_param = self.grad_op_param_sens(self.loss_net, self.params_head)(head_input, label, sens)
-    self.optimizer_head(grad_head_param)
-
-    # update of cut layer
-
-    tmp = grad_head_input.flatten().asnumpy().tolist()
-    grad_input = backward_tee_cut_layer(remote_emb.shape[0], remote_emb.shape[1], local_emb.shape[1], 1, tmp)
-    grad_inputa = self.reshape(Tensor(grad_input[0]), remote_emb.shape)
-    grad_inputb = self.reshape(Tensor(grad_input[1]), local_emb.shape)
-    grad_cutlayer_input = (grad_inputb[:, :1], grad_inputb[:, 1:2], grad_inputa[:, :1], grad_inputa[:, 1:2])
-
-    # update of bottom net
-    grad_bottom_wide = self.grad_op_param_sens(self.net.bottom_net,
-                                               self.params_bottom_wide)(id_hldr, wt_hldr,
-                                                                        grad_cutlayer_input[0:2])
-    self.optimizer_bottom_wide(grad_bottom_wide)
-    grad_bottom_deep = self.grad_op_param_sens(self.net.bottom_net,
-                                               self.params_bottom_deep)(id_hldr, wt_hldr,
-                                                                        grad_cutlayer_input[0:2])
-    grad_bottom_l2 = self.grad_op_param_sens(self.loss_net_l2, self.params_bottom_deep)(sens)
-    zipped = zip(grad_bottom_deep, grad_bottom_l2)
-    grad_bottom_deep = tuple(map(sum, zipped))
-    self.optimizer_bottom_deep(grad_bottom_deep)
-
-    # output the gradients for follower party
-    scales = {}
-    scales['wide_loss'] = OrderedDict(zip(['wide_embedding', 'deep_embedding'], grad_cutlayer_input[2:4]))
-    scales['deep_loss'] = scales['wide_loss']
-    return scales
-```
-
-#### Definig the Optimizer
-
-When defining the optimizer, there is no need to define the backward propagation part covered by `grad_network` in the yaml file, otherwise there is no difference with the usual vfl model to define the optimizer.
-
-### Constructing the Training Script
-
-#### Constructing the Network
-
-As the usual vFL training, users need to use the classes provided by MindSpore Federated to wrap their constructed networks into a vertical federated network. Detailed API documentation can be found in [Vertical Federated Training Interface](https://gitee.com/mindspore/federated/blob/master/docs/api/api_python_en/vertical/vertical_federated_FLModel.rst). The difference is that when constructing the leader network, you need to add `grad_network`.
-
-```python
-from mindspore_federated import FLModel, FLYamlData
-from network_config import config
-from wide_and_deep import LeaderNet, LeaderLossNet, LeaderGradNet
-
-
-leader_base_net = LeaderNet(config)
-leader_train_net = LeaderLossNet(leader_base_net, config)
-leader_grad_net = LeaderGradNet(leader_base_net, config)
-
-leader_yaml_data = FLYamlData(config.leader_yaml_path)
-leader_fl_model = FLModel(yaml_data=leader_yaml_data,
-                          network=leader_base_net,
-                          grad_network=Leader_grad_net,
-                          train_network=leader_train_net)
-```
-
-Except for the above, the rest of TEE training is identical to the usual vFL training, and the user can enjoy the security of TEE once the configuration is completed.
diff --git a/docs/federated/docs/source_en/sentiment_classification_application.md b/docs/federated/docs/source_en/sentiment_classification_application.md
deleted file mode 100644
index 30ae58fe1f..0000000000
--- a/docs/federated/docs/source_en/sentiment_classification_application.md
+++ /dev/null
@@ -1,563 +0,0 @@
-# Implementing a Sentiment Classification Application (Android)
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/sentiment_classification_application.md)
-
-Through the federated learning modeling approach of cross-device collaboration, the advantages of device-side data can be fully utilized to avoid uploading sensitive user data directly to the cloud side. Since users attach great importance to the privacy of the text they input when using input methods, and the intelligent functions of input methods are important to improve user experience. Therefore, federated learning is naturally applicable to the input method application scenarios.
-
-MindSpore Federated has applied the Federated Language Model to the emoji image prediction feature of the input method. The Federated Language Model recommends emoji images that are appropriate for the current context based on chat text data. When modeling with federated learning, each emoji image is defined as a sentiment label category, and each chat phrase corresponds to an emoji image. MindSpore Federated defines the emoji image prediction task as a federated sentiment classification task.
-
-## Preparations
-
-### Environment
-
-For details, see [Server Environment Configuration](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_server.html) and [Client Environment Configuration](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_client.html).
-
-### Data
-
-The [training data](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/supervise/client.tar.gz) contains 20 user chat files. The directory structure is as follows:
-
-```text
-datasets/supervise/client/
-    ├── 0.txt  # Training data of user 0
-    ├── 1.txt  # Training data of user 1
-    │
-    │          ......
-    │
-    └── 19.txt  # Training data of user 19
-```
-
-The [validation data](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/supervise/eval.tar.gz) contains one chat file. The directory structure is as follows:
-
-```text
-datasets/supervise/eval/
-    └── eval.txt  # Validation data
-```
-
-The labels in the training data and validation data correspond to four types of emojis: `good`, `leimu`, `xiaoku`, `xin`.
-
-### Model-related Files
-
-The directory structures of the [dictionary](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/vocab.txt) and the [mapping file of dictionary ID](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/vocab_map_ids.txt) related to the model file are as follows:
-
-```text
-datasets/
-    ├── vocab.txt  # Dictionary
-    └── vocab_map_ids.txt  # Mapping file of Dictionary ID
-```
-
-## Defining the Network
-
-The ALBERT language model[1] is used in federated learning. The ALBERT model on the client includes the embedding layer, encoder layer, and classifier layer.
-
-For details about the network definition, see [source code](https://gitee.com/mindspore/federated/blob/master/tests/st/network/albert.py).
-
-### Generating a Device-Side Model File
-
-User can generate a Device-Side Model File as follow, or download the generated [ALBERT Device-Side Model File](https://gitee.com/link?target=https%3A%2F%2Fmindspore-website.obs.cn-north-4.myhuaweicloud.com%2Fnotebook%2Fmodels%2Falbert_supervise.mindir.ms).
-
-#### Exporting a Model as a MindIR File
-
-The sample code is as follows:
-
-```python
-import argparse
-import os
-import random
-from time import time
-import numpy as np
-import mindspore as ms
-from mindspore.nn import AdamWeightDecay
-from src.config import train_cfg, client_net_cfg
-from src.utils import restore_params
-from src.model import AlbertModelCLS
-from src.cell_wrapper import NetworkWithCLSLoss, NetworkTrainCell
-
-
-def parse_args():
-    """
-    parse args
-    """
-    parser = argparse.ArgumentParser(description='export task')
-    parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'])
-    parser.add_argument('--device_id', type=str, default='0')
-    parser.add_argument('--init_model_path', type=str, default='none')
-    parser.add_argument('--output_dir', type=str, default='./models/mindir/')
-    parser.add_argument('--seed', type=int, default=0)
-    return parser.parse_args()
-
-
-def supervise_export(args_opt):
-    ms.set_seed(args_opt.seed), random.seed(args_opt.seed)
-    start = time()
-    # Parameter configuration
-    os.environ['CUDA_VISIBLE_DEVICES'] = args_opt.device_id
-    init_model_path = args_opt.init_model_path
-    output_dir = args_opt.output_dir
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    print('Parameters setting is done! Time cost: {}'.format(time() - start))
-    start = time()
-
-    # MindSpore configuration
-    ms.set_context(mode=ms.GRAPH_MODE, device_target=args_opt.device_target)
-    print('Context setting is done! Time cost: {}'.format(time() - start))
-    start = time()
-
-    # Build model
-    albert_model_cls = AlbertModelCLS(client_net_cfg)
-    network_with_cls_loss = NetworkWithCLSLoss(albert_model_cls)
-    network_with_cls_loss.set_train(True)
-    print('Model construction is done! Time cost: {}'.format(time() - start))
-    start = time()
-
-    # Build optimizer
-    client_params = [_ for _ in network_with_cls_loss.trainable_params()]
-    client_decay_params = list(
-        filter(train_cfg.optimizer_cfg.AdamWeightDecay.decay_filter, client_params)
-    )
-    client_other_params = list(
-        filter(lambda x: not train_cfg.optimizer_cfg.AdamWeightDecay.decay_filter(x), client_params)
-    )
-    client_group_params = [
-        {'params': client_decay_params, 'weight_decay': train_cfg.optimizer_cfg.AdamWeightDecay.weight_decay},
-        {'params': client_other_params, 'weight_decay': 0.0},
-        {'order_params': client_params}
-    ]
-    client_optimizer = AdamWeightDecay(client_group_params,
-                                       learning_rate=train_cfg.client_cfg.learning_rate,
-                                       eps=train_cfg.optimizer_cfg.AdamWeightDecay.eps)
-    client_network_train_cell = NetworkTrainCell(network_with_cls_loss, optimizer=client_optimizer)
-    print('Optimizer construction is done! Time cost: {}'.format(time() - start))
-    start = time()
-
-    # Construct data
-    input_ids = ms.Tensor(np.zeros((train_cfg.batch_size, client_net_cfg.seq_length), np.int32))
-    attention_mask = ms.Tensor(np.zeros((train_cfg.batch_size, client_net_cfg.seq_length), np.int32))
-    token_type_ids = ms.Tensor(np.zeros((train_cfg.batch_size, client_net_cfg.seq_length), np.int32))
-    label_ids = ms.Tensor(np.zeros((train_cfg.batch_size,), np.int32))
-    print('Client data loading is done! Time cost: {}'.format(time() - start))
-    start = time()
-
-    # Read checkpoint
-    if init_model_path != 'none':
-        init_param_dict = ms.load_checkpoint(init_model_path)
-        restore_params(client_network_train_cell, init_param_dict)
-    print('Checkpoint loading is done! Time cost: {}'.format(time() - start))
-    start = time()
-
-    # Export
-    ms.export(client_network_train_cell, input_ids, attention_mask, token_type_ids, label_ids,
-           file_name=os.path.join(output_dir, 'albert_supervise'), file_format='MINDIR')
-    print('Supervise model export process is done! Time cost: {}'.format(time() - start))
-
-
-if __name__ == '__main__':
-    total_time_start = time()
-    args = parse_args()
-    supervise_export(args)
-    print('All is done! Time cost: {}'.format(time() - total_time_start))
-
-```
-
-#### Converting the MindIR File into an MS File that Can be Used by the Federated Learning Framework on the Device
-
-For details about how to generate a model file on the device, see [Implementing an Image Classification Application](https://www.mindspore.cn/federated/docs/en/master/image_classification_application.html).
-
-## Starting the Federated Learning Process
-
-Start the script on the server. For details, see [Cloud-based Deployment](https://www.mindspore.cn/federated/docs/en/master/deploy_federated_server.html).
-For corresponding cloud-side configuration and model weights document, refer to [albert example](https://gitee.com/mindspore/federated/tree/master/example/cross_device_albert).
-
-Based on the training and inference tasks of the ALBERT model, the overall process is as follows:
-
-1. Create an Android project.
-
-2. Build the MindSpore Lite AAR package.
-
-3. Describe the Android instance program structure.
-
-4. Write code.
-
-5. Configure Android project dependencies.
-
-6. Build and run on Android.
-
-### Creating an Android Project
-
-Create a project in Android Studio and install the corresponding SDK. (After the SDK version is specified, Android Studio automatically installs the SDK.)
-
-![New project](./images/create_android_project.png)
-
-### Obtaining a Related Package
-
-1. Obtain MindSpore Lite AAR package
-
-   For details, see [MindSpore Lite](https://www.mindspore.cn/lite/docs/en/master/use/downloads.html).
-
-   ```text
-   mindspore-lite-full-{version}.aar
-   ```
-
-2. Obtain MindSpore Federated device-side jar package
-
-   For details, see [On-Device Deployment](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html).
-
-   ```text
-   mindspore_federated/device_client/build/libs/jarAAR/mindspore-lite-java-flclient.jar
-   ```
-
-3. Place the AAR package in the app/libs/ directory of the Android project.
-
-### Android Instance Program Structure
-
-```text
-app
-│   ├── libs # Binary archive file of the Android library project
-|   |   ├── mindspore-lite-full-{version}.aar #  MindSpore Lite archive file of the Android version
-|   |   └── mindspore-lite-java-flclient.jar #  MindSpore Federate archive file of the Android version
-├── src/main
-│   ├── assets # Resource directory
-|   |   └── model # Model directory
-|   |       └── albert_supervise.mindir.ms # Pre-trained model file
-│   |       └── albert_inference.mindir.ms # Inference model file
-│   |   └── data # Data directory
-|   |       └── 0.txt # training data file
-|   |       └── vocab.txt # Dictionary file
-|   |       └── vocab_map_ids.txt # Dictionary ID mapping file
-|   |       └── eval.txt # Training result evaluation file
-|   |       └── eval_no_label.txt # Inference data file
-│   |
-│   ├── java # Application code at the Java layer
-│   │       └── ... Storing Android code files. Related directories can be customized.
-│   │
-│   ├── res # Resource files related to Android
-│   └── AndroidManifest.xml # Android configuration file
-│
-│
-├── build.gradle # Android project build file
-├── download.gradle # Downloading the project dependency files
-└── ...
-```
-
-### Writing Code
-
-1. AssetCopyer.java: This code file is used to store the resource files in the app/src/main/assets directory of the Android project to the disk of the Android system. In this way, the federated learning framework API can read the resource files based on the absolute path during model training and inference.
-
-    ```java
-    import android.content.Context;
-    import java.io.File;
-    import java.io.FileOutputStream;
-    import java.io.InputStream;
-    import java.util.logging.Logger;
-    public class AssetCopyer {
-        private static final Logger LOGGER = Logger.getLogger(AssetCopyer.class.toString());
-        public static void copyAllAssets(Context context,String destination) {
-            LOGGER.info("destination: " + destination);
-            copyAssetsToDst(context,"",destination);
-        }
-        // Copy the resource files in the assets directory to the disk of the Android system. You can view the specific path by printing destination.
-        private static void copyAssetsToDst(Context context,String srcPath, String dstPath) {
-            try {
-                // Recursively obtain all file names in the assets directory.
-                String[] fileNames =context.getAssets().list(srcPath);
-                if (fileNames.length > 0) {
-                    // Build the destination file object.
-                    File file = new File(dstPath);
-                    // Create a destination directory.
-                    file.mkdirs();
-                    for (String fileName : fileNames) {
-                        // Copy the file to the specified disk.
-                        if(!srcPath.equals("")) {
-                            copyAssetsToDst(context,srcPath + "/" + fileName,dstPath+"/"+fileName);
-                        }else{
-                            copyAssetsToDst(context, fileName,dstPath+"/"+fileName);
-                        }
-                    }
-                } else {
-                    // Build the input stream of the source file.
-                    InputStream is = context.getAssets().open(srcPath);
-                    // Build the output stream of the destination file.
-                    FileOutputStream fos = new FileOutputStream(new File(dstPath));
-                    // Define a 1024-byte buffer array.
-                    byte[] buffer = new byte[1024];
-                    int byteCount=0;
-                    // Write the source file to the destination file.
-                    while((byteCount=is.read(buffer))!=-1) {
-                        fos.write(buffer, 0, byteCount);
-                    }
-                    // Refresh the output stream.
-                    fos.flush();
-                    // Close the input stream.
-                    is.close();
-                    // Close the output stream.
-                    fos.close();
-                }
-            } catch (Exception e) {
-                e.printStackTrace();
-            }
-        }
-    }
-    ```
-
-2. FlJob.java: This code file is used to define training and inference tasks. For details about federated learning APIs, see [federated Learning APIs](https://www.mindspore.cn/federated/docs/en/master/interface_description_federated_client.html).
-
-    ```java
-    import android.annotation.SuppressLint;
-    import android.os.Build;
-    import androidx.annotation.RequiresApi;
-    import com.mindspore.flAndroid.utils.AssetCopyer;
-    import com.mindspore.flclient.FLParameter;
-    import com.mindspore.flclient.SyncFLJob;
-    import java.util.Arrays;
-    import java.util.UUID;
-    import java.util.logging.Logger;
-    public class FlJob {
-        private static final Logger LOGGER = Logger.getLogger(AssetCopyer.class.toString());
-        private final String parentPath;
-        public FlJob(String parentPath) {
-            this.parentPath = parentPath;
-        }
-        // Android federated learning training task
-        @SuppressLint("NewApi")
-        @RequiresApi(api = Build.VERSION_CODES.M)
-        public void syncJobTrain() {
-            // create dataMap
-            String trainTxtPath = "data/albert/supervise/client/1.txt";
-            String evalTxtPath = "data/albert/supervise/eval/eval.txt";      // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-            String vocabFile = "data/albert/supervise/vocab.txt";                // Path of the dictionary file for data preprocessing.
-            String idsFile = "data/albert/supervise/vocab_map_ids.txt"   // Path of the mapping ID file of a dictionary.
-            Map> dataMap = new HashMap<>();
-            List trainPath = new ArrayList<>();
-            trainPath.add(trainTxtPath);
-            trainPath.add(vocabFile);
-            trainPath.add(idsFile);
-            List evalPath = new ArrayList<>();    // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-            evalPath.add(evalTxtPath);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-            evalPath.add(vocabFile);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-            evalPath.add(idsFile);                  // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-            dataMap.put(RunType.TRAINMODE, trainPath);
-            dataMap.put(RunType.EVALMODE, evalPath);      // Not necessary, if you don't need verify model accuracy after getModel, you don't need to set this parameter
-
-            String flName = "com.mindspore.flclient.demo.albert.AlbertClient";                             // The package path of AlBertClient.java
-            String trainModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path
-            String inferModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path, consistent with trainModelPath
-            String sslProtocol = "TLSv1.2";
-            String deployEnv = "android";
-
-            // The url for device-cloud communication. Ensure that the Android device can access the server. Otherwise, the message "connection failed" is displayed.
-            String domainName = "http://10.*.*.*:6668";
-            boolean ifUseElb = true;
-            int serverNum = 4;
-            int threadNum = 4;
-            BindMode cpuBindMode = BindMode.NOT_BINDING_CORE;
-            int batchSize = 32;
-
-            FLParameter flParameter = FLParameter.getInstance();
-            flParameter.setFlName(flName);
-            flParameter.setDataMap(dataMap);
-            flParameter.setTrainModelPath(trainModelPath);
-            flParameter.setInferModelPath(inferModelPath);
-            flParameter.setSslProtocol(sslProtocol);
-            flParameter.setDeployEnv(deployEnv);
-            flParameter.setDomainName(domainName);
-            flParameter.setUseElb(ifUseElb);
-            flParameter.setServerNum(serverNum);
-            flParameter.setThreadNum(threadNum);
-            flParameter.setCpuBindMode(BindMode.valueOf(cpuBindMode));
-
-            // start FLJob
-            SyncFLJob syncFLJob = new SyncFLJob();
-            syncFLJob.flJobRun();
-        }
-        // Android federated learning inference task
-        public void syncJobPredict() {
-            // create dataMap
-            String inferTxtPath = "data/albert/supervise/eval/eval.txt";
-            String vocabFile = "data/albert/supervise/vocab.txt";
-            String idsFile = "data/albert/supervise/vocab_map_ids.txt";
-            Map> dataMap = new HashMap<>();
-            List inferPath = new ArrayList<>();
-            inferPath.add(inferTxtPath);
-            inferPath.add(vocabFile);
-            inferPath.add(idsFile);
-            dataMap.put(RunType.INFERMODE, inferPath);
-
-            String flName = "com.mindspore.flclient.demo.albert.AlbertClient";                             // The package path of AlBertClient.java
-            String inferModelPath = "ms/albert/train/albert_ad_train.mindir0.ms";                      // Absolute path, consistent with trainModelPath
-            int threadNum = 4;
-            BindMode cpuBindMode = BindMode.NOT_BINDING_CORE;
-            int batchSize = 32;
-
-            FLParameter flParameter = FLParameter.getInstance();
-            flParameter.setFlName(flName);
-            flParameter.setDataMap(dataMap);
-            flParameter.setInferModelPath(inferModelPath);
-            flParameter.setThreadNum(threadNum);
-            flParameter.setCpuBindMode(cpuBindMode);
-            flParameter.setBatchSize(batchSize);
-
-            // inference
-            SyncFLJob syncFLJob = new SyncFLJob();
-            int[] labels = syncFLJob.modelInference();
-            LOGGER.info("labels = " + Arrays.toString(labels));
-        }
-    }
-    ```
-
-    The above eval_no_label.txt refers to a file where no label exists, with one statement per line. The format reference is as follows, which the user is free to set:
-
-    ```text
-    愿以吾辈之青春 护卫这盛世之中华🇨🇳
-    girls help girls
-    太美了，祝祖国繁荣昌盛！
-    中国人民站起来了
-    难道就我一个人觉得这个是plus版本？
-    被安利到啦！明天起来就看！早点睡觉莲莲
-    ```
-
-3. MainActivity.java: This code file is used to start federated learning training and inference tasks.
-
-    ```java
-    import android.os.Build;
-    import android.os.Bundle;
-    import androidx.annotation.RequiresApi;
-    import androidx.appcompat.app.AppCompatActivity;
-    import com.huawei.flAndroid.job.FlJob;
-    import com.huawei.flAndroid.utils.AssetCopyer;
-    @RequiresApi(api = Build.VERSION_CODES.P)
-    public class MainActivity extends AppCompatActivity {
-        private String parentPath;
-        @Override
-        protected void onCreate(Bundle savedInstanceState) {
-            super.onCreate(savedInstanceState);
-            // Obtain the disk path of the application in the Android system.
-            this.parentPath = this.getExternalFilesDir(null).getAbsolutePath();
-            // Copy the resource files in the assets directory to the disk of the Android system.
-            AssetCopyer.copyAllAssets(this.getApplicationContext(), parentPath);
-            // Create a thread and start the federated learning training and inference tasks.
-            new Thread(() -> {
-                FlJob flJob = new FlJob(parentPath);
-                flJob.syncJobTrain();
-                flJob.syncJobPredict();
-            }).start();
-        }
-    }
-    ```
-
-### Configuring Android Project Dependencies
-
-1. AndroidManifest.xml
-
-    ```xml
-    
-    
-        
-        
-        
-            
-            
-                
-                    
-                    
-                
-            
-        
-    
-    ```
-
-2. app/build.gradle
-
-    ```text
-    plugins {
-        id 'com.android.application'
-    }
-    android {
-        // Android SDK build version. It is recommended that the version be later than 27.
-        compileSdkVersion 30
-        buildToolsVersion "30.0.3"
-        defaultConfig {
-            applicationId "com.mindspore.flAndroid"
-            minSdkVersion 27
-            targetSdkVersion 30
-            versionCode 1
-            versionName "1.0"
-            multiDexEnabled true
-            testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
-            ndk {
-                // Different mobile phone models correspond to different NDKs. Mate 20 corresponds to 'armeabi-v7a'.
-                abiFilters 'armeabi-v7a'
-            }
-        }
-        // Specified NDK version
-        ndkVersion '21.3.6528147'
-        sourceSets{
-            main {
-                // Specified JNI directory
-                jniLibs.srcDirs = ['libs']
-                jni.srcDirs = []
-            }
-        }
-        compileOptions {
-            sourceCompatibility JavaVersion.VERSION_1_8
-            targetCompatibility JavaVersion.VERSION_1_8
-        }
-    }
-    dependencies {
-        // AAR package to be scanned in the libs directory
-        implementation fileTree(dir:'libs',include:['*.aar', '*.jar'])
-        implementation 'androidx.appcompat:appcompat:1.1.0'
-        implementation 'com.google.android.material:material:1.1.0'
-        implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
-        androidTestImplementation 'androidx.test.ext:junit:1.1.1'
-        androidTestImplementation 'androidx.test.espresso:espresso-core:3.2.0'
-        implementation 'com.android.support:multidex:1.0.3'
-
-        // Add third-party open source software that federated learning relies on
-        implementation group: 'com.squareup.okhttp3', name: 'okhttp', version: '3.14.9'
-        implementation group: 'com.google.flatbuffers', name: 'flatbuffers-java', version: '2.0.0'
-        implementation(group: 'org.bouncycastle',name: 'bcprov-jdk15on', version: '1.68')
-    }
-    ```
-
-### Building and Running on Android
-
-1. Connect to the Android device and run federated learning training and inference applications. Connect to the Android device through a USB cable for debugging. Click `Run 'app'` to run the federated learning task on your device.
-
-    ![run_app](./images/start_android_project.png)
-
-2. For details about how to connect the Android Studio to a device for debugging, see . Android Studio can identify the mobile phone only when USB debugging mode is enabled on the mobile phone. For Huawei phones, enable USB debugging mode by choosing `Settings > System & updates > Developer options > USB debugging`.
-
-3. Continue the installation on the Android device. After the installation is complete, you can start the app to train and infer the ALBERT model for federated learning.
-
-4. The program running result is as follows:
-
-   ```text
-   I/SyncFLJob:  [model inference] inference finish
-   I/SyncFLJob: labels = [2, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 4, 4, 4, 4]
-   ```
-
-## Results
-
-The total number of federated learning iterations is 10, the number of client-side local training epochs is 1, and the batchSize is set to 16.
-
-```text
- total acc:0.44488978
- total acc:0.583166333
- total acc:0.609218437
- total acc:0.645290581
- total acc:0.667334669
- total acc:0.685370741
- total acc:0.70741483
- total acc:0.711422846
- total acc:0.719438878
- total acc:0.733466934
-```
-
-## References
-
-[1] Lan Z, Chen M , Goodman S, et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations[J].  2019.
diff --git a/docs/federated/docs/source_en/split_pangu_alpha_application.md b/docs/federated/docs/source_en/split_pangu_alpha_application.md
deleted file mode 100644
index 3f9594025a..0000000000
--- a/docs/federated/docs/source_en/split_pangu_alpha_application.md
+++ /dev/null
@@ -1,338 +0,0 @@
-# Vertical Federated Learning Model Training - Pangu Alpha Large Model Cross-Domain Training
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/split_pangu_alpha_application.md)
-
-## Overview
-
-With the advancement of hardware computing power and the continuous expansion of network data size, pre-training large models has increasingly become an important research direction in fields such as natural language processing and graphical multimodality. Take Pangu Alpha, which released a large pre-trained model of Chinese NLP in 2021, as an example, the number of model parameters reaches 200 billion, and the training process relies on massive data and advanced computing centers, which limits its application landing and technology evolution. A feasible solution is to integrate the computing power and data resources of multiple participants based on vertical federated learning or split learning techniques to achieve cross-domain collaborative training of pre-trained large models while ensuring security and privacy.
-
-MindSpore Federated provides a vertical federated learning base functional component based on split learning. This sample provides a federated learning training sample for large NLP models by taking the Pangaea alpha model as an example.
-
-![Implementing cross-domain training for the Pangu Alpha large model](./images/splitnn_pangu_alpha_en.png)
-
-As shown in the figure above, in this case, the Pangaea α model is sliced into three sub-networks, such as Embedding, Backbone and Head. The front-level subnetwork Embedding and the end-level subnetwork Head are deployed in the network domain of participant A, and the Backbone subnetwork containing multi-level Transformer modules is deployed in the network domain of participant B. The Embedding subnetwork and Head subnetwork read the data held by participant A and dominate the training and inference tasks for performing the Pangaea α model.
-
-* In the forward inference stage, Participant A uses the Embedding subnetwork to process the original data and transmits the output Embedding Feature tensor and Attention Mask Feature tensor to Participant B as the input of Participant B Backbone subnetwork. Then, Participant A reads the Hide State Feature tensor output from the Backbone subnetwork as the input of Participant A Head subnetwork, and finally the predicted result or loss value is output by the Head sub-network.
-
-* In the backward propagation phase, after completing the gradient calculation and parameter update of the Head subnetwork, Participant A transmits the gradient tensor associated with the Hide State Feature tensor to Participant B for the gradient calculation and parameter update of the Backbone subnetwork. Then, Participant B transmits the gradient tensor associated with the Embedding Feature tensor to Participant A for the gradient calculation and parameter update of the Embedding subnetwork after completing the gradient calculation and parameter update of the Backbone subnetwork.
-
-The feature tensor and gradient tensor exchanged between participant A and participant B during the above forward inference and backward propagation are processed by using privacy security mechanisms and encryption algorithms, so that it is not necessary to transmit the data held by participant A to participant B for implementing the collaboration training of the network model by the two participants. Due to the small number of Embedding and Head subnetwork parameters and the huge number of Backbone subnetwork parameters, this sample application is suitable for the large model collaboration training or deployment between the service side (corresponding to participant A) and the computing center (corresponding to participant B).
-
-For a detailed introduction to the pangu α model principles, please refer to [MindSpore ModelZoo - pangu_alpha](https://gitee.com/mindspore/models/tree/master/official/nlp/Pangu_alpha), [Introduction to Pengcheng -pangu α](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha), and its [research paper](https://arxiv.org/pdf/2104.12369.pdf).
-
-## Preparation
-
-### Environment Preparation
-
-1. Refer to [Obtaining MindSpore Federated](https://mindspore.cn/federated/docs/en/master/federated_install.html) to install MindSpore version 1.8.1 and above and MindSpore Federated.
-
-2. Download the MindSpore Federated code and install the Python packages that this sample application depends on.
-
-    ```bash
-    git https://gitee.com/mindspore/federated.git
-    cd federated/example/splitnn_pangu_alpha/
-    python -m pip install -r requirements.txt
-    ```
-
-### Dataset Preparation
-
-Before running the sample, refer to [MindSpore ModelZoo - pangu_alpha - Dataset Generation](https://gitee.com/mindspore/models/tree/master/official/nlp/Pangu_alpha#dataset-generation) and use the preprocess.py script to convert the raw text corpus for training into a dataset that can be used for model training.
-
-## Defining the Vertical Federated Learning Training Process
-
-MindSpore Federated Vertical Federated Learning Framework uses FLModel (see [Vertical Federated Learning Model Training Interface](https://mindspore.cn/federated/docs/en/master/vertical/vertical_federated_FLModel.html)) and yaml files (see [Yaml Configuration file for model training of vertical federated learning](https://mindspore.cn/federated/docs/en/master/vertical/vertical_federated_yaml.html)), to model vertical federated learning training process.
-
-### Defining the Network Model
-
-1. Call the function components provided by MindSpore and take nn.Cell (see [mindspore.nn.Cell](https://mindspore.cn/docs/en/master/api_python/nn/mindspore.nn.Cell.html#mindspore-nn-cell)) as a base class to program the training network of this participant to be involved in vertical federated learning. Taking the Embedding subnetwork of participant A in this application practice as an example, [sample code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/src/split_pangu_alpha.py) is as follows:
-
-   ```python
-   class EmbeddingLossNet(nn.Cell):
-       """
-       Train net of the embedding party, or the tail sub-network.
-       Args:
-           net (class): EmbeddingLayer, which is the 1st sub-network.
-           config (class): default config info.
-       """
-
-       def __init__(self, net: EmbeddingLayer, config):
-           super(EmbeddingLossNet, self).__init__(auto_prefix=False)
-
-           self.batch_size = config.batch_size
-           self.seq_length = config.seq_length
-           dp = config.parallel_config.data_parallel
-           self.eod_token = config.eod_token
-           self.net = net
-           self.slice = P.StridedSlice().shard(((dp, 1),))
-           self.not_equal = P.NotEqual().shard(((dp, 1), ()))
-           self.batch_size = config.batch_size
-           self.len = config.seq_length
-           self.slice2 = P.StridedSlice().shard(((dp, 1, 1),))
-
-       def construct(self, input_ids, position_id, attention_mask):
-           """forward process of FollowerLossNet"""
-           tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1))
-           embedding_table, word_table = self.net(tokens, position_id, batch_valid_length=None)
-           return embedding_table, word_table, position_id, attention_mask
-   ```
-
-2. In the yaml configuration file, describe the corresponding name, input, output and other information of the training network. Taking the Embedding subnetwork of Participant A in this application practice, [example code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/embedding.yaml) is as follows:
-
-   ```yaml
-   train_net:
-       name: follower_loss_net
-       inputs:
-         - name: input_ids
-           source: local
-         - name: position_id
-           source: local
-         - name: attention_mask
-           source: local
-       outputs:
-         - name: embedding_table
-           destination: remote
-         - name: word_table
-           destination: remote
-         - name: position_id
-           destination: remote
-         - name: attention_mask
-           destination: remote
-   ```
-
-    The `name` field is the name of the training network and will be used to name the checkpoints file saved during the training process. The `inputs` field is the list of input tensor in the training network, and the `outputs` field is the list of output tensor in the training network.
-
-    The `name` fields under the `inputs` and `outputs` fields are the input/output tensor names. The names and order of the input/output tensors need to correspond strictly to the inputs/outputs of the `construct` method in the corresponding Python code of the training network.
-
-    `source` under the `inputs` field identifies the data source of the input tensor, with `local` representing that the input tensor is loaded from local data and `remote` representing that the input tensor is from network transmission of other participants.
-
-    `destination` under the `outputs` field identifies the destination of the output tensor, with `local` representing the output tensor for local use only, and `remote` representing that the output tensor is transferred to other participants via networks.
-
-3. Optionally, a similar approach is used to model the assessment network of vertical federated learning that this participant is to be involved.
-
-### Defining the Optimizer
-
-1. Call the functional components provided by MindSpore, to program the optimizer for parameter updates of this participant training network. As an example of a custom optimizer used by Participant A for Embedding subnetwork training in this application practice, [sample code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/src/pangu_optim.py) is as follows:
-
-    ```python
-    class PanguAlphaAdam(TrainOneStepWithLossScaleCell):
-        """
-        Customized Adam optimizer for training of pangu_alpha in the splitnn demo system.
-        """
-        def __init__(self, net, optim_inst, scale_update_cell, config, yaml_data) -> None:
-            # Custom optimizer-related operators
-            ...
-
-        def __call__(self, *inputs, sens=None):
-            # Define the gradient calculation and parameter update process
-            ...
-    ```
-
-    Developers can customize the input and output of the `__init__` method in the optimizer class, but the input of the `__call__` method in the optimizer class needs to contain only `inputs` and `sens`. `inputs` is of type `list`, corresponding to the input tensor list of the training network, and its elements are of type `mindspore.Tensor`. `sens` is of type `dict`, which saves the weighting coefficients used to calculate the gradient values of the training network parameters, and its key is a gradient weighting coefficient identifier of type `str`. Value is of type `dict`, whose key is of type `str`, and it is the name of the output tensor of the training network. Value is of type `mindspore.Tensor`, which is the weighting coefficient of the training network parameter gradient values corresponding to this output tensor.
-
-2. In the yaml configuration file, describe the corresponding gradient calculation, parameter update, and other information of the optimizer. The [sample code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/embedding.yaml) is as follows:
-
-    ```yaml
-    opts:
-      - type: CustomizedAdam
-        grads:
-          - inputs:
-              - name: input_ids
-              - name: position_id
-              - name: attention_mask
-            output:
-              name: embedding_table
-            sens: hidden_states
-          - inputs:
-              - name: input_ids
-              - name: position_id
-              - name: attention_mask
-            output:
-              name: word_table
-            sens: word_table
-        params:
-          - name: word_embedding
-          - name: position_embedding
-        hyper_parameters:
-          learning_rate: 5.e-6
-          eps: 1.e-8
-          loss_scale: 1024.0
-    ```
-
-    The `type` field is of the optimizer type. Here is the developer-defined optimizer.
-
-    The `grads` field is a list of `GradOperation` associated with the optimizer, which will use the `GradOperation` operator in the list to compute the output gradient values and update the training network parameters. The `inputs` and `output` fields are input and output tensor lists of the `GradOperation` operator, whose elements are an input/output tensor name, respectively. The `sens` field is the gradient weighting coefficient or the sensitivity identifier of the `GradOperation` operator (refer to [mindspore.ops.GradOperation](https://mindspore.cn/docs/en/master/api_python/ops/mindspore.ops.GradOperation.html?highlight=gradoperation)).
-
-    The `params` field is a list of training network parameter names to be updated by the optimizer, whose elements are the names of one training network parameter each. In this example, the custom optimizer will update the network parameters with the `word_embedding` string and the `position_embedding` string in their names.
-
-    The `hyper_parameters` field is a list of hyperparameters for the optimizer.
-
-### Defining Gradient Weighting Coefficient Calculation
-
-According to the chain rule of gradient calculation, the subnetwork located at the backstream of the global network needs to calculate the gradient value of its output tensor relative to the input tensor, i.e., the gradient weighting coefficient or sensitivity, to be passed to the sub-network located at the upstream of the global network for its training parameter update.
-
-MindSpore Federated uses the `GradOperation` operator to complete the above gradient weighting coefficient or sensitivity calculation process. The developer needs to describe the `GradOperation` operator used to calculate the gradient weighting coefficients in the yaml configuration file. Taking Head of participant A in this application practice as an example, [sample code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/head.yaml) is as follows:
-
-```yaml
-grad_scalers:
-  - inputs:
-      - name: hidden_states
-      - name: input_ids
-      - name: word_table
-      - name: position_id
-      - name: attention_mask
-    output:
-      name: output
-    sens: 1024.0
-```
-
-The `inputs` and `output` fields are lists of input and output tensors of the `GradOperation` operator, whose elements are input/output tensor names, respectively. The `sens` field is the gradient weighting coefficient or sensitivity of this `GradOperation` operator (refer to [mindspore.ops.GradOperation](https://mindspore.cn/docs/en/master/api_python/ops/mindspore.ops.GradOperation.html?highlight=gradoperation)). If it is a `float` or `int` type value, a constant tensor will be constructed as the gradient weighting coefficient. If it is a `str` type string, the tensor corresponding to the name will be parsed as a weighting coefficient from the weighting coefficients transmitted by the other participants via the network.
-
-### Executing the Training
-
-1. After completing the above Python programming development and yaml configuration file, the `FLModel` class and `FLYamlData` class provided by MindSpore Federated are used to build the vertical federated learning process. Taking the Embedding subnetwork of participant A in this application practice as an example, [sample code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/run_pangu_train_local.py) is as follows:
-
-    ```python
-    embedding_yaml = FLYamlData('./embedding.yaml')
-    embedding_base_net = EmbeddingLayer(config)
-    embedding_eval_net = embedding_train_net = EmbeddingLossNet(embedding_base_net, config)
-    embedding_with_loss = _VirtualDatasetCell(embedding_eval_net)
-    embedding_params = embedding_with_loss.trainable_params()
-    embedding_group_params = set_embedding_weight_decay(embedding_params)
-    embedding_optim_inst = FP32StateAdamWeightDecay(embedding_group_params, lr, eps=1e-8, beta1=0.9, beta2=0.95)
-    embedding_optim = PanguAlphaAdam(embedding_train_net, embedding_optim_inst, update_cell, config, embedding_yaml)
-
-    embedding_fl_model = FLModel(yaml_data=embedding_yaml,
-                                 network=embedding_train_net,
-                                 eval_network=embedding_eval_net,
-                                 optimizers=embedding_optim)
-    ```
-
-    The `FLYamlData` class mainly completes the parsing and verification of yaml configuration files, and the `FLModel` class mainly provides the control interface for vertical federated learning training, inference and other processes.
-
-2. Call the interface methods of the `FLModel` class to perform vertical federated learning training. Taking the Embedding subnetwork of participant A in this application practice as an example, [sample code](https://gitee.com/mindspore/federated/blob/master/example/splitnn_pangu_alpha/run_pangu_train_local.py) is as follows:
-
-    ```python
-    if opt.resume:
-        embedding_fl_model.load_ckpt()
-        ...
-    for epoch in range(50):
-        for step, item in enumerate(train_iter, start=1):
-            # forward process
-            step = epoch * train_size + step
-            embedding_out = embedding_fl_model.forward_one_step(item)
-            ...
-            # backward process
-            embedding_fl_model.backward_one_step(item, sens=backbone_scale)
-            ...
-            if step % 1000 == 0:
-                embedding_fl_model.save_ckpt()
-    ```
-
-    The `forward_one_step` method and the `backward_one_step` method perform the forward inference and backward propagation operations of a data batch, respectively. The `load_ckpt` method and the `save_ckpt` method perform the checkpoints file loading and saving operations respectively.
-
-## Running the Example
-
-This example provides 2 sample programs, both running as shell scripts to pull up Python programs.
-
-1. `run_pangu_train_local.sh`: Single-process example program. Participant A and participant B are trained in the same process, which transmits the feature tensor and gradient tensor directly to the other participant in the form of intra-program variables.
-
-2. `run_pangu_train_leader.sh` and `run_pangu_train_follower.sh`: Multi-process example program. Participant A and participant B run a separate process, which encapsulates the feature tensor and gradient tensor as protobuf messages, respectively, and transmits them to the other participant via the https communication interface. `run_pangu_train_leader.sh` and `run_pangu_train_follower.sh` can be run on two servers separately to achieve cross-domain collaboration training.
-
-3. The current vertical federated distributed training supports https cross-domain encrypted communication. The startup command is as follows:
-
-   ```bash
-   # Start the leader process in https encrypted communication mode:
-   bash run_pangu_train_leader.sh 127.0.0.1:10087 127.0.0.1:10086 /path/to/train/data_set /path/to/eval/data_set True server_cert_password client_cert_password /path/to/server_cert /path/to/client_cert /path/to/ca_cert
-
-   # Start the follower process in https encrypted communication mode:
-   bash run_pangu_train_follower.sh 127.0.0.1:10086 127.0.0.1:10087 True server_cert_password client_cert_password /path/to/server_cert /path/to/client_cert /path/to/ca_cert
-   ```
-
-### Running a Single-Process Example
-
-Taking `run_pangu_train_local.sh` as an example, run the sample program as follows:
-
-1. Go to the sample program directory:
-
-    ```bash
-    cd federated/example/splitnn_pangu_alpha/
-    ```
-
-2. Taking the wiki dataset as an example, copy the dataset to the sample program directory:
-
-    ```bash
-    cp -r {dataset_dir}/wiki ./
-    ```
-
-3. Install the dependent Python packages:
-
-    ```bash
-    python -m pip install -r requirements.txt
-    ```
-
-4. Modify `src/utils.py` to configure parameters such as checkpoint file load path, training dataset path, and evaluation dataset path. Examples are as follows:
-
-    ```python
-    parser.add_argument("--load_ckpt_path", type=str, default='./checkpoints', help="predict file path.")
-    parser.add_argument('--data_url', required=False, default='./wiki/train/', help='Location of data.')
-    parser.add_argument('--eval_data_url', required=False, default='./wiki/eval/', help='Location of eval data.')
-    ```
-
-5. Execute the training script:
-
-    ```bash
-    ./run_pangu_train_local.sh
-    ```
-
-6. View the training loss information recorded in the training log `splitnn_pangu_local.txt`.
-
-    ```text
-    INFO:root:epoch 0 step 10/43391 loss: 10.616087
-    INFO:root:epoch 0 step 20/43391 loss: 10.424824
-    INFO:root:epoch 0 step 30/43391 loss: 10.209235
-    INFO:root:epoch 0 step 40/43391 loss: 9.950026
-    INFO:root:epoch 0 step 50/43391 loss: 9.712448
-    INFO:root:epoch 0 step 60/43391 loss: 9.557744
-    INFO:root:epoch 0 step 70/43391 loss: 9.501564
-    INFO:root:epoch 0 step 80/43391 loss: 9.326054
-    INFO:root:epoch 0 step 90/43391 loss: 9.387547
-    INFO:root:epoch 0 step 100/43391 loss: 8.795234
-    ...
-    ```
-
-    The corresponding visualization results are shown below, where the horizontal axis is the number of training steps, the vertical axis is the loss value, the red curve is the Pangu α training loss value, and the blue curve is the Pangu α training loss value based on splitting learning in this example. The trend of decreasing loss values is basically the same, and the correctness of the training process can be verified considering that the initialization of the network parameter values has randomness.
-
-    ![Cross-domain training results of the Pangu alpha large model](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/federated/docs/source_zh_cn/images/splitnn_pangu_alpha_result.png)
-
-### Running a Multi-Process Example
-
-1. Similar to the single-process example, go to the sample program directory, and install the dependent Python packages:
-
-    ```bash
-    cd federated/example/splitnn_pangu_alpha/
-    python -m pip install -r requirements.txt
-    ```
-
-2. Copy the dataset to the sample program directory on Server 1:
-
-   ```bash
-    cp -r {dataset_dir}/wiki ./
-   ```
-
-3. Start the training script for Participant A on Server 1:
-
-    ```bash
-    ./run_pangu_train_leader.sh {ip_address_server1} {ip_address_server2} ./wiki/train ./wiki/train
-    ```
-
-    The first parameter of the training script is the IP address and port number of the local server (Server 1), and the second parameter is the IP address and port number of the peer server (Server 2). The third parameter is the training dataset file path. The fourth parameter is the evaluation dataset file path, and the fifth parameter identifies whether to load an existing checkpoint file.
-
-4. Start the training script for Participant B on Server 2.
-
-    ```bash
-    ./run_pangu_train_follower.sh {ip_address_server2} {ip_address_server1}
-    ```
-
-    The first parameter of the training script is the IP address and port number of the local server (Server 2), and the second parameter is the IP address and port number of the peer server (Server 2). The third parameter identifies whether to load an existing checkpoint file.
-
-5. Check the training loss information recorded in the training log `leader_processs.log` of Server 1. If the trend of its loss information is consistent with that of the centralized training loss values of Pangaea α, the correctness of the training process can be verified.
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/split_wnd_application.md b/docs/federated/docs/source_en/split_wnd_application.md
deleted file mode 100644
index 13b8f996ee..0000000000
--- a/docs/federated/docs/source_en/split_wnd_application.md
+++ /dev/null
@@ -1,280 +0,0 @@
-# Vertical Federated Learning Model Training - Wide&Deep Recommendation Application
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/split_wnd_application.md)
-
-## Overview
-
-MindSpore Federated provides a vertical federated learning infrastructure component based on Split Learning.
-
-Vertical FL model training scenarios: including two stages of forward propagation and backward propagation/parameter update.
-
-Forward propagation: After the data intersection module processes the parameter-side data and aligns the feature information and label information, the Follower participant inputs the local feature information into the precursor network model, and the feature tensor output from the precursor network model is encrypted/scrambled by the privacy security module and transmitted to the Leader participant by the communication module. The Leader participants input the received feature tensor into the post-level network model, and the predicted values and local label information output from the post-level network model are used as the loss function input to calculate the loss values.
-
-![](./images/vfl_forward_en.png)
-
-Backward propagation: The Leader participant calculates the parameter gradient of the backward network model based on the loss value, trains and updates the parameters of the backward network model, and transmits the gradient tensor associated with the feature tensor to the Follower participant by the communication module after encrypted and scrambled by the privacy security module. The Follower participant uses the received gradient tensor for training and update of of frontward network model parameters.
-
-![](./images/vfl_backward_en.png)
-
-Vertical FL model inference scenario: similar to the forward propagation phase of the training scenario, but with the predicted values of the backward network model directly as the output, without calculating the loss values.
-
-## Network and Data
-
-![](./images/splitnn_wide_and_deep_en.png)
-
-This sample provides a federated learning training example for recommendation-oriented tasks by using Wide&Deep network and Criteo dataset as examples. As shown above, in this case, the vertical federated learning system consists of the Leader participant and the Follower participant. Among them, the Leader participant holds 20×2 dimensional feature information and label information, and the Follower participant holds 19×2 dimensional feature information. Leader participant and Follower participant deploy 1 set of Wide&Deep network respectively, and realize the collaborative training of the network model by exchanging embedding vectors and gradient vectors without disclosing the original features and label information.
-
-For a detailed description of the principle properties of Wide&Deep networks, see [MindSpore ModelZoo - Wide&Deep - Wide&Deep Overview](https://gitee.com/mindspore/models/blob/master/official/recommend/Wide_and_Deep/README.md#widedeep-description) and its [research paper](https://arxiv.org/pdf/1606.07792.pdf).
-
-## Dataset Preparation
-
-This sample is based on the Criteo dataset for training and testing. Before running the sample, you need to refer to [MindSpore ModelZoo - Wide&Deep - Quick Start](https://gitee.com/mindspore/models/blob/master/official/recommend/Wide_and_Deep/README.md#quick-start) to pre-process the Criteo dataset.
-
-1. Clone MindSpore ModelZoo code.
-
-   ```shell
-   git clone https://gitee.com/mindspore/models.git
-   cd models/official/recommend/Wide_and_Deep
-   ```
-
-2. Download the dataset
-
-   ```shell
-   mkdir -p data/origin_data && cd data/origin_data
-   wget http://go.criteo.net/criteo-research-kaggle-display-advertising-challenge-dataset.tar.gz
-   tar -zxvf criteo-research-kaggle-display-advertising-challenge-dataset.tar.gz
-   ```
-
-3. Use this script to pre-process the data. The preprocessing process may take up to an hour and the generated MindRecord data is stored in the data/mindrecord path. The preprocessing process consumes a lot of memory, so it is recommended to use a server.
-
-   ```shell
-   cd ../..
-   python src/preprocess_data.py  --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
-   ```
-
-## Quick Experience
-
-This sample runs as a Shell script pulling up a Python program.
-
-1. Refer to [MindSpore website guidance](https://www.mindspore.cn/install), installing MindSpore 1.8.1 or higher.
-
-2. Use to install the Python libraries that MindSpore Federated depends on.
-
-   ```shell
-   cd federated
-   python -m pip install -r requirements_test.txt
-   ```
-
-3. Copy the Criteo dataset after [preprocessing](#dataset-preparation) to this directory.
-
-   ```shell
-   cd tests/example/splitnn_criteo
-   cp -rf ${DATA_ROOT_PATH}/data/mindrecord/ ./
-   ```
-
-4. Run the sample program to start the script.
-
-   ```shell
-   # start leader:
-   bash run_vfl_train_leader.sh 127.0.0.1:10087 127.0.0.1:10086 /path/to/data_set False
-
-   # start follower:
-   bash run_vfl_train_follower.sh 127.0.0.1:10086 127.0.0.1:10087 /path/to/data_set False
-   ```
-
-   or
-
-   ```shell
-   # Start the leader process with https encrypted communication:
-   bash run_vfl_train_leader.sh 127.0.0.1:10087 127.0.0.1:10086 /path/to/data_set True server_cert_password client_cert_password /path/to/server_cert /path/to/client_cert /path/to/ca_cert
-
-   # Start the follower process using https encrypted communication:
-   bash run_vfl_train_follower.sh 127.0.0.1:10086 127.0.0.1:10087 /path/to/data_set True server_cert_password client_cert_password /path/to/server_cert /path/to/client_cert /path/to/ca_cert
-   ```
-
-5. View training log `log_local_gpu.txt`.
-
-   ```text
-   INFO:root:epoch 0 step 100/2582 wide_loss: 0.528141 deep_loss: 0.528339
-   INFO:root:epoch 0 step 200/2582 wide_loss: 0.499408 deep_loss: 0.499410
-   INFO:root:epoch 0 step 300/2582 wide_loss: 0.477544 deep_loss: 0.477882
-   INFO:root:epoch 0 step 400/2582 wide_loss: 0.474377 deep_loss: 0.476771
-   INFO:root:epoch 0 step 500/2582 wide_loss: 0.472926 deep_loss: 0.475157
-   INFO:root:epoch 0 step 600/2582 wide_loss: 0.464844 deep_loss: 0.467011
-   INFO:root:epoch 0 step 700/2582 wide_loss: 0.464496 deep_loss: 0.466615
-   INFO:root:epoch 0 step 800/2582 wide_loss: 0.466895 deep_loss: 0.468971
-   INFO:root:epoch 0 step 900/2582 wide_loss: 0.463155 deep_loss: 0.465299
-   INFO:root:epoch 0 step 1000/2582 wide_loss: 0.457914 deep_loss: 0.460132
-   INFO:root:epoch 0 step 1100/2582 wide_loss: 0.453361 deep_loss: 0.455767
-   INFO:root:epoch 0 step 1200/2582 wide_loss: 0.457566 deep_loss: 0.459997
-   INFO:root:epoch 0 step 1300/2582 wide_loss: 0.460841 deep_loss: 0.463281
-   INFO:root:epoch 0 step 1400/2582 wide_loss: 0.460973 deep_loss: 0.463365
-   INFO:root:epoch 0 step 1500/2582 wide_loss: 0.459204 deep_loss: 0.461563
-   INFO:root:epoch 0 step 1600/2582 wide_loss: 0.456771 deep_loss: 0.459200
-   INFO:root:epoch 0 step 1700/2582 wide_loss: 0.458479 deep_loss: 0.460963
-   INFO:root:epoch 0 step 1800/2582 wide_loss: 0.449609 deep_loss: 0.452122
-   INFO:root:epoch 0 step 1900/2582 wide_loss: 0.451775 deep_loss: 0.454225
-   INFO:root:epoch 0 step 2000/2582 wide_loss: 0.460343 deep_loss: 0.462826
-   INFO:root:epoch 0 step 2100/2582 wide_loss: 0.456814 deep_loss: 0.459201
-   INFO:root:epoch 0 step 2200/2582 wide_loss: 0.452091 deep_loss: 0.454555
-   INFO:root:epoch 0 step 2300/2582 wide_loss: 0.461522 deep_loss: 0.464001
-   INFO:root:epoch 0 step 2400/2582 wide_loss: 0.442355 deep_loss: 0.444790
-   INFO:root:epoch 0 step 2500/2582 wide_loss: 0.450675 deep_loss: 0.453242
-   ...
-   ```
-
-6. Close training process.
-
-   ```shell
-   pid=`ps -ef|grep run_vfl_train_socket |grep -v "grep" | grep -v "finish" |awk '{print $2}'` && for id in $pid; do kill -9 $id && echo "killed $id"; done
-   ```
-
-## Deep Experience
-
-Before starting the vertical federated learning training, users need to construct the dataset iterator and network structure as they do for normal deep learning training with MindSpore.
-
-### Building the Dataset
-
-The current simulation process is used, i.e., both participants read the same data source. But for training, both participants use only part of the feature or label data, as shown in [Network and Data](#network-and-data). Later, the [Data Access](https://www.mindspore.cn/federated/docs/en/master/data_join/data_join.html) method will be used for both participants to import the data individually.
-
-```python
-from run_vfl_train_local import construct_local_dataset
-
-
-ds_train, _ = construct_local_dataset()
-train_iter = ds_train.create_dict_iterator()
-```
-
-### Building the Network
-
-Leader participant network:
-
-```python
-from wide_and_deep import WideDeepModel, BottomLossNet, LeaderTopNet, LeaderTopLossNet, LeaderTopEvalNet, \
-     LeaderTeeNet, LeaderTeeLossNet, LeaderTopAfterTeeNet, LeaderTopAfterTeeLossNet, LeaderTopAfterTeeEvalNet, \
-     AUCMetric
-from network_config import config
-
-
-# Leader Top Net
-leader_top_base_net = LeaderTopNet()
-leader_top_train_net = LeaderTopLossNet(leader_top_base_net)
-...
-# Leader Bottom Net
-leader_bottom_eval_net = leader_bottom_base_net = WideDeepModel(config, config.leader_field_size)
-leader_bottom_train_net = BottomLossNet(leader_bottom_base_net, config)
-```
-
-Follower participant network:
-
-```python
-from wide_and_deep import WideDeepModel, BottomLossNet
-from network_config import config
-
-
-follower_bottom_eval_net = follower_base_net = WideDeepModel(config, config.follower_field_size)
-follower_bottom_train_net = BottomLossNet(follower_base_net, config)
-```
-
-### Vertical Federated Communication Base
-
-Before training, we first have to start the communication base to make Leader and Follower participants group network. Detailed API documentation can be found in [Vertical Federated Communicator](https://gitee.com/mindspore/federated/blob/master/docs/api/api_python_en/vertical/vertical_communicator.rst).
-
-Both parties need to import the vertical federated communicator:
-
-```python
-from mindspore_federated.startup.vertical_federated_local import VerticalFederatedCommunicator, ServerConfig
-```
-
-Leader participant communication base:
-
-```python
-http_server_config = ServerConfig(server_name='leader', server_address=config.http_server_address)
-remote_server_config = ServerConfig(server_name='follower', server_address=config.remote_server_address)
-self.vertical_communicator = VerticalFederatedCommunicator(http_server_config=http_server_config,
-                                                           remote_server_config=remote_server_config,
-                                                           compress_configs=compress_configs)
-self.vertical_communicator.launch()
-```
-
-Follower participant communication base:
-
-```python
-http_server_config = ServerConfig(server_name='follower', server_address=config.http_server_address)
-remote_server_config = ServerConfig(server_name='leader', server_address=config.remote_server_address)
-self.vertical_communicator = VerticalFederatedCommunicator(http_server_config=http_server_config,
-                                                           remote_server_config=remote_server_config,
-                                                           compress_configs=compress_configs)
-self.vertical_communicator.launch()
-```
-
-### Building a Vertical Federated Network
-
-Users need to use the classes provided by MindSpore Federated to wrap their constructed networks into a vertical federated network. The detailed API documentation can be found in [Vertical Federated Training Interface](https://gitee.com/mindspore/federated/blob/master/docs/api/api_python_en/vertical/vertical_federated_FLModel.rst).
-
-Both parties need to import the vertical federated training interface:
-
-```python
-from mindspore_federated import FLModel, FLYamlData
-```
-
-Leader participant vertical federated network:
-
-```python
-leader_bottom_yaml_data = FLYamlData(config.leader_bottom_yaml_path)
-leader_top_yaml_data = FLYamlData(config.leader_top_yaml_path)
-...
-self.leader_top_fl_model = FLModel(yaml_data=leader_top_yaml_data,
-                                   network=leader_top_train_net,
-                                   metrics=self.eval_metric,
-                                   eval_network=leader_top_eval_net)
-...
-self.leader_bottom_fl_model = FLModel(yaml_data=leader_bottom_yaml_data,
-                                      network=leader_bottom_train_net,
-                                      eval_network=leader_bottom_eval_net)
-```
-
-Follower participant vertical federated network:
-
-```python
-follower_bottom_yaml_data = FLYamlData(config.follower_bottom_yaml_path)
-...
-self.follower_bottom_fl_model = FLModel(yaml_data=follower_bottom_yaml_data,
-                                        network=follower_bottom_train_net,
-                                        eval_network=follower_bottom_eval_net)
-```
-
-### Vertical Training
-
-For the process of vertical training, refer to [overview](#overview).
-
-Leader participant training process:
-
-```python
-for epoch in range(config.epochs):
-    for step, item in enumerate(train_iter):
-        leader_embedding = self.leader_bottom_fl_model.forward_one_step(item)
-        item.update(leader_embedding)
-        follower_embedding = self.vertical_communicator.receive("follower")
-        ...
-        leader_out = self.leader_top_fl_model.forward_one_step(item, follower_embedding)
-        grad_scale = self.leader_top_fl_model.backward_one_step(item, follower_embedding)
-        scale_name = 'loss'
-        ...
-        grad_scale_follower = {scale_name: OrderedDict(list(grad_scale[scale_name].items())[2:])}
-        self.vertical_communicator.send_tensors("follower", grad_scale_follower)
-        grad_scale_leader = {scale_name: OrderedDict(list(grad_scale[scale_name].items())[:2])}
-        self.leader_bottom_fl_model.backward_one_step(item, sens=grad_scale_leader)
-```
-
-Follower participant training process:
-
-```python
-for _ in range(config.epochs):
-    for _, item in enumerate(train_iter):
-        follower_embedding = self.follower_bottom_fl_model.forward_one_step(item)
-        self.vertical_communicator.send_tensors("leader", follower_embedding)
-        scale = self.vertical_communicator.receive("leader")
-        self.follower_bottom_fl_model.backward_one_step(item, sens=scale)
-```
-
diff --git a/docs/federated/docs/source_en/vertical_federated_trainer.rst b/docs/federated/docs/source_en/vertical_federated_trainer.rst
deleted file mode 100644
index ccd9b3c96b..0000000000
--- a/docs/federated/docs/source_en/vertical_federated_trainer.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-Vertical Federated Trainer
-==========================
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/vertical_federated_trainer.rst
-    :alt: View Source On Gitee
-
-.. toctree::
-   :maxdepth: 1
-
-   vertical/vertical_federated_FLModel
-   vertical/vertical_federated_yaml
\ No newline at end of file
diff --git a/docs/federated/docs/source_en/vfl_communication_compress.md b/docs/federated/docs/source_en/vfl_communication_compress.md
deleted file mode 100644
index ae13e0c55d..0000000000
--- a/docs/federated/docs/source_en/vfl_communication_compress.md
+++ /dev/null
@@ -1,231 +0,0 @@
-# Vertical Federated Learning Communication Compression
-
-[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/vfl_communication_compress.md)
-
-Vertical federated learning traffic affects user experience (user traffic, communication latency, federated learning training efficiency) and is limited by performance constraints (memory, bandwidth, CPU usage). Small amount of communication helps a lot to improve user experience and reduce performance bottlenecks, so it needs to be compressed. MindSpore Federated implements bi-directional communication compression between Leader and Follower in a vertical federated application scenario.
-
-## Overall Process
-
-![image1](./images/vfl_normal_communication_compress_en.png)
-
-Figure 1 Framework diagram of general vertical federated learning communication compression process
-
-First perform the Embedding DP (EDP) encryption operation on Follower. Then enter the bit packing process. The bit packing process automatically determines whether the input data can be packed, and only when the input data can be strongly converted to the specified bit storage format with no loss of precision, the bit packing operation is performed. Follower sends the packaged data to Leader, Leader determines whether the data needs to be unpacked based on the reported data information. Before the Leader passes the data to the Follower, it is quantized and compressed. Follower receives the data and decompresses the quantized data.
-
-![image2](./images/vfl_pangu_communication_compress_en.png)
-
-Figure 2 Framework diagram of Pangu vertical federated learning communication compression process
-
-The overall process is the same as the general vertical federated learning communication compression process. Compared to normal vertical federated, each iteration will have one more round of communication in Pangu vertical federated learning, so it needs to perform one more quantization compression and decompression process.
-
-## Compression Method
-
-### Bit Packing Compression Method
-
-Bit-packing compression is a method of converting a sequence of data structures into a compact binary representation. Bit packing itself is a lossless compression method, but usually the data input to bit packing is compressed with loss compression.
-
-Taking 3-bit packing as an example:
-
-Quantization bitness bit_num= 3
-
-The data stored in the format of float32 before compression is:
-
-data = [3, -4, 3, -2, 3, -2, -4, 0, 1, 3]
-
-First determine if compression is possible using bit-packing compression:
-
-data_int = int(data)
-
-If the elements in data - data_int are not 0, exit the bit packing process.
-
-Convert the source data to binary format based on bit_num:
-
-data_bin = [011, 100, 011, 110, 011, 110, 100, 000, 001, 011]
-
-Note: Before conversion, you need to determine whether the current data is within the range that bit_num can accommodate. If it exceeds the range exit the bit packing process.
-
-Since native C++ does not have a dedicated binary storage format, multiple binary data need to be stitched together and combined into int8 format data storage. If the number of bits is not enough, zero is added to the last data. Combined data is as follows:
-
-data_int8 = [01110001, 11100111, 10100000, 00101100]
-
-The binary data is then converted to an integer between -128 and 127, and the data type is strongly converted to int8.
-
-data_packed = [113, -25, -96, 44]
-
-Finally, data_packed and bit_num are passed to the receiver.
-
-When unpacking, the receiver simply reverses the above process.
-
-### Quantization Compression Method
-
-The quantization compression method is that communication data fixpoint of floating is approximated to a finite number of discrete values. The currently supported quantization compression method is minimum-maximum compression (min_max).
-
-Taking the 8-bit quantization as an example:
-
-Quantization bitness bit_num= 8
-
-The float data before compression is:
-
-data = [0.03356021, -0.01842778, -0.009684053, 0.025363436, -0.027571501, 0.0077043395, 0.016391572, -0.03598478, -0.0009508357]
-
-Compute the minimum and maximum values:
-
-min_val = -0.03598478
-
-max_val = 0.03356021
-
-Compute the scaling coefficient:
-
-scale = (max_val - min_val) / (2 ^ bit_num- 1) = 0.000272725450980392
-
-Convert the pre-compressed data to an integer between -128 and 127 with the formula quant_data = round((data - min_val) / scale) - 2 ^ (bit_num - 1). And to directly convert the data type to int8:
-
-quant_data = [127, -64, -32, 97, -97, 32, 64, -128, 0]
-
-After the quantitative encoding, the parameters to be uploaded by the sender are quant_data, bit_num, and the maximum and minimum values min_val and max_val.
-
-After receiving quant_data, min_val and max_val, the receiver uses the inverse quantization formula (quant_data + 2 ^ (bit_num - 1)) * (max_val - min_val) / (2 ^ bit_num - 1) + min_val to restore the weights.
-
-## Fast Experience
-
-To use bit packing or quantized compression methods, one first needs to successfully complete the training aggregation process for any of the vertical federated scenarios, such as [Vertical Federated Learning Model Training - Wide&Deep Recommended Applications](https://www.mindspore.cn/federated/docs/en/master/split_wnd_application.html). The preparation work including datasets and network models and the process of simulating the start of federated learning are described in detail in this document.
-
-1. For MindSpore and MindSpore Federated installation and data preprocessing, refer to [Vertical Federated Learning Model Training - Wide&Deep Recommended Applications](https://www.mindspore.cn/federated/docs/en/master/split_wnd_application.html).
-
-2. Set the configuration related to compression setting in [related yaml](https://gitee.com/mindspore/federated/tree/master/example/splitnn_criteo/yaml_files).
-
-   The configuration of [leader_top.yaml](https://gitee.com/mindspore/federated/blob/master/example/splitnn_criteo/yaml_files/leader_top.yaml) is as follows:
-
-   ```yaml
-   role: leader
-   model: # define the net of vFL party
-     train_net:
-       name: leader_loss_net
-       inputs:
-         - name: leader_wide_embedding
-           source: local
-         - name: leader_deep_embedding
-           source: local
-         - name: follower_wide_embedding
-           source: remote
-           compress_type: min_max
-           bit_num: 6
-         - name: follower_deep_embedding
-           source: remote
-           compress_type: min_max
-           bit_num: 6
-   ...
-   ```
-
-   The configuration of [follower_bottom.yaml](https://gitee.com/mindspore/federated/blob/master/example/splitnn_criteo/yaml_files/follower_bottom.yaml) is as follows:
-
-   ```yaml
-   role: follower
-   model: # define the net of vFL party
-     train_net:
-       name: follower_loss_net
-       inputs:
-         - name: id_hldr0
-           source: local
-         - name: wt_hldr0
-           source: local
-       outputs:
-         - name: follower_wide_embedding
-           destination: remote
-           compress_type: min_max
-           bit_num: 6
-         - name: follower_deep_embedding
-           destination: remote
-           compress_type: min_max
-           bit_num: 6
-         - name: follower_l2_regu
-           destination: local
-   ...
-   ```
-
-3. Users can make super-reference modifications according to the actual situation.
-
-   - compress_type: Compression type, string type, including: "min_max", "bit_pack".
-   - bit_num: The number of bits, int type, the definition domain is within [1, 8].
-
-4. Run the sample program to start the script.
-
-   ```shell
-   # Start leader process:
-   bash run_vfl_train_leader.sh 127.0.0.1:1984 127.0.0.1:1230 ./mindrecord/ False
-   # Start follower process:
-   bash run_vfl_train_follower.sh 127.0.0.1:1230 127.0.0.1:1984 ./mindrecord/ False
-   ```
-
-5. Check the training log `vfl_train_leader.log`. loss converges normally.
-
-   ```text
-   epoch 0 step 0 loss: 0.693124
-   epoch 0 step 100 loss: 0.512151
-   epoch 0 step 200 loss: 0.493524
-   epoch 0 step 300 loss: 0.473054
-   epoch 0 step 400 loss: 0.466222
-   epoch 0 step 500 loss: 0.464252
-   epoch 0 step 600 loss: 0.469296
-   epoch 0 step 700 loss: 0.451647
-   epoch 0 step 800 loss: 0.457797
-   epoch 0 step 900 loss: 0.457930
-   epoch 0 step 1000 loss: 0.461664
-   epoch 0 step 1100 loss: 0.460415
-   epoch 0 step 1200 loss: 0.466883
-   epoch 0 step 1300 loss: 0.455919
-   epoch 0 step 1400 loss: 0.466984
-   epoch 0 step 1500 loss: 0.454486
-   epoch 0 step 1600 loss: 0.458730
-   epoch 0 step 1700 loss: 0.451275
-   epoch 0 step 1800 loss: 0.445938
-   epoch 0 step 1900 loss: 0.458323
-   epoch 0 step 2000 loss: 0.446709
-   ...
-   ```
-
-6. Close the training process
-
-   ```shell
-   pid=`ps -ef|grep run_vfl_train_ |grep -v "grep" | grep -v "finish" |awk '{print $2}'` && for id in $pid; do kill -9 $id && echo "killed $id"; done
-   ```
-
-## Deep Experience
-
-### Obtaining the Compression Configuration
-
-The user can use the encapsulated interface to get the configuration related to communication compression. The [Yaml Configuration file for model training of vertical federated learning](https://www.mindspore.cn/federated/docs/en/master/vertical/vertical_federated_yaml.html) gives the configuration description of the parameters related to the startup. The [Model Training Interface](https://www.mindspore.cn/federated/docs/en/master/vertical/vertical_federated_FLModel.html) provides the interface to get the compression configuration. The example method is as follows:
-
-```python
-# parse yaml files
-leader_top_yaml_data = FLYamlData(config.leader_top_yaml_path)
-
-# Leader Top Net
-leader_top_base_net = LeaderTopNet()
-leader_top_train_net = LeaderTopLossNet(leader_top_base_net)
-leader_top_fl_model = FLModel(
-    yaml_data=leader_top_yaml_data,
-    network=leader_top_train_net
-)
-
-# get compress config
-compress_configs = leader_top_fl_model.get_compress_configs()
-```
-
-### Setting Compression Configuration
-
-Users can use the already encapsulated [Vertical Federated Learning Communicator](https://www.mindspore.cn/federated/docs/en/master/vertical/vertical_communicator.html) interface to set the configuration related to communication compression to the communicator device by the following method:
-
-```python
-# build vertical communicator
-http_server_config = ServerConfig(server_name='leader', server_address=config.http_server_address)
-remote_server_config = ServerConfig(server_name='follower', server_address=config.remote_server_address)
-vertical_communicator = VerticalFederatedCommunicator(
-    http_server_config=http_server_config,
-    remote_server_config=remote_server_config,
-    compress_configs=compress_configs
-)
-vertical_communicator.launch()
-```
-
-After setting the communication compression configuration, the vertical federated framework will automatically compress the communication content in the backend.
diff --git a/docs/federated/docs/source_zh_cn/Data_Join.rst b/docs/federated/docs/source_zh_cn/Data_Join.rst
deleted file mode 100644
index e1a33a58b0..0000000000
--- a/docs/federated/docs/source_zh_cn/Data_Join.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-数据求交
-=====================
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/Data_Join.rst
-    :alt: 查看源文件
-
-.. toctree::
-   :maxdepth: 1
-
-   data_join/data_join
-   data_join/private_set_intersection
\ No newline at end of file
diff --git a/docs/federated/docs/source_zh_cn/_ext/__pycache__/my_signature.cpython-37.pyc b/docs/federated/docs/source_zh_cn/_ext/__pycache__/my_signature.cpython-37.pyc
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diff --git a/docs/federated/docs/source_zh_cn/communication_compression.md b/docs/federated/docs/source_zh_cn/communication_compression.md
deleted file mode 100644
index c64fd7bd5c..0000000000
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+++ /dev/null
@@ -1,137 +0,0 @@
-# 端云联邦学习通信压缩
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/communication_compression.md)
-
-在横向的端云联邦学习训练过程中，通信量会影响端侧用户体验（用户流量、通信时延、FL-Client 参与数量），并受云侧性能约束（内存、带宽、CPU 占用率）限制。为了提高用户体验和减少性能瓶颈，MindSpore联邦学习框架在端云联邦场景中，提供上传和下载的通信量压缩功能。
-
-## 压缩方法
-
-### 上传压缩方法
-
-上传压缩方法可以分为三个主要部分：权重差编解码、稀疏编解码和量化编解码，下面给出了FL-Client和FL-Server上的流程图。
-
-![上传压缩client执行顺序](./images/upload_compression_client.png)
-
-图1 上传压缩方法在FL-Client上的流程图
-
-![上传压缩server执行顺序](./images/upload_compression_server.png)
-
-图2 上传压缩方法在FL-Server上的流程图
-
-### 权重差编解码
-
-权重差即为端侧训练前后的权重矩阵的向量差。相较于原始权重而言，权重差的分布更符合高斯分布，因此更适合被压缩。FL-Client对权重差进行编码操作，FL-Server对权重差进行解码操作。值得注意的是，为了在FL-Server聚合权重前就将权重差还原为权重，FL-Client在上传权重时，不将权重乘以数据量。FL-Server解码时，需要将权重乘以数据量。
-
-![权重差编码](./images/weight_diff_encode.png)
-
-图3 权重差编码在FL-Client上的流程图
-
-![权重差解码](./images/weight_diff_decode.png)
-
-图4 权重差解码在FL-Server上的流程图
-
-### 稀疏编解码
-
-端云都遵循同样的随机算法生成稀疏掩码矩阵，该掩码矩阵和原本需要上传的权重形状相同。掩码矩阵只包含0或1两个值。每个FL-Client只上传和掩码矩阵非零值位置相同的权重的数据到FL-Server上。
-
-以稀疏率sparse_rate=0.08的稀疏方法为例。FL-Client原本需要上传的参数：
-
-| 参数名               | 长度  |
-| -------------------- | ----- |
-| albert.pooler.weight | 97344 |
-| albert.pooler.bias   | 312   |
-| classifier.weight    | 1560  |
-| classifier.bias      | 5     |
-
-将所有参数接为一维向量：
-
-| 参数名      | 长度                   |
-| ----------- | ---------------------- |
-| merged_data | 97344+312+1560+5=99221 |
-
-生成和拼接后参数长度一样的mask向量，其中，有7937 = int(sparse_rate*拼接后参数长度)个值为1，其余值为0（即mask_vector = (1,1,1,...,0,0,0,...)）：
-
-| 参数名      | 长度  |
-| ----------- | ----- |
-| mask_vector | 99221 |
-
-使用伪随机算法，将mask_vector随机打乱。随机种子为当前的iteration数。取出mask_vector中值为1的索引indexes。取出merged_data[indexes]的值，即压缩后的向量：
-
-| 参数名            | 长度 |
-| ----------------- | ---- |
-| compressed_vector | 7937 |
-
-稀疏压缩后，FL-Client需要上传的参数即为compressed_vector。
-
-FL-Server在收到compressed_vector后，首先会用和FL-Client同样的伪随机算法和随机种子，构造出掩码向量mask_vector。然后取出mask_vector中值为1的索引indexes。再然后，生成和模型相同shape的全零矩阵weight_vector。依次将compressed_vector中的值放入weight_vector[indexes]中。weight_vector即为稀疏解码后的向量。
-
-### 量化编解码
-
-量化压缩方法即将浮点型的通信数据定点近似为有限多个离散值。
-
-以8-bit量化举例来讲：
-
-量化位数num_bits = 8
-
-压缩前的浮点型数据为：
-
-data = [0.03356021, -0.01842778, -0.009684053, 0.025363436, -0.027571501, 0.0077043395, 0.016391572, -0.03598478,  -0.0009508357]
-
-计算最大和最小值：
-
-min_val = -0.03598478
-
-max_val = 0.03356021
-
-计算缩放系数：
-
-scale = (max_val - min_val ) / (2 ^ num_bits - 1) = 0.000272725450980392
-
-将压缩前数据转换为-128到127之间的整数，转换公式为quant_data = round((data - min_val) / scale) - 2 ^ (num_bits - 1)。并强转数据类型到int8：
-
-quant_data = [127, -64, -32, 97, -97, 32, 64, -128, 0]
-
-量化编码后，FL-Client需要上传的参数即为quant_data以及最小和最大值min_val和max_val。
-
-FL-Server在收到quant_data、min_val和max_val后，使用反量化公式(quant_data + 2 ^ (num_bits - 1)) * (max_val - min_val) / (2 ^ num_bits - 1) + min_val，还原出权重。
-
-## 下载压缩方法
-
-下载压缩方法主要为量化编解码操作，下面给出了FL-Server和FL-Client上的流程图。
-
-![下载压缩server执行顺序](./images/download_compression_server.png)
-
-图5 下载压缩方法在FL-Server上的流程图
-
-![下载压缩client执行顺序](./images/download_compression_client.png)
-
-图6 下载压缩方法在FL-Client上的流程图
-
-### 量化编解码
-
-量化的编解码方法和上传压缩中一样。
-
-## 代码实现准备工作
-
-若要使用上传和下载压缩方法，首先需要成功完成任一端云联邦场景的训练聚合过程，如[实现一个情感分类应用(Android)](https://www.mindspore.cn/federated/docs/zh-CN/master/sentiment_classification_application.html)。在该文档中详细介绍了包括数据集和网络模型等准备工作和模拟启动多客户端参与联邦学习的流程。
-
-## 算法开启脚本
-
-上传和下载压缩方法目前只支持端云联邦学习场景。开启方式需要在启动云侧服务时，在server启动脚本中，在对应的yaml中设置`upload_compress_type='DIFF_SPARSE_QUANT'`和`download_compress_type='QUANT'`。上述两个超参数即可分别控制上传和下载压缩方法的开启和关闭。云侧[完整启动脚本](https://gitee.com/mindspore/federated/tree/master/tests/st/cross_device_cloud/)，这里给出启动该算法的相关参数配置。确定参数配置后，用户需要在执行训练前配置对应参数，具体如下：
-
-```yaml
-compression:
-  upload_compress_type: NO_COMPRESS
-  upload_sparse_rate: 0.4
-  download_compress_type: NO_COMPRESS
-```
-
-| 超参名称&参考值        | 超参描述                                                     |
-| ---------------------- | ------------------------------------------------------------ |
-| upload_compress_type   | 上传压缩类型，string类型，包括："NO_COMPRESS"、"DIFF_SPARSE_QUANT" |
-| upload_sparse_rate     | 稀疏率，即权重保留率，float类型，定义域在(0, 1]内            |
-| download_compress_type | 下载压缩类型，string类型，包括："NO_COMPRESS"、"QUANT"       |
-
-## ALBERT实验结果
-
-联邦学习总迭代数为100，客户端本地训练epoch数为1，客户端数量为20，batchSize设置为16，学习率为1e-5，同时开启上传和下载压缩方法，上传稀疏率为0.4。最终在验证集上的准确率为72.5%，不压缩的普通联邦场景为72.3%。
\ No newline at end of file
diff --git a/docs/federated/docs/source_zh_cn/conf.py b/docs/federated/docs/source_zh_cn/conf.py
deleted file mode 100644
index ea938ec4f1..0000000000
--- a/docs/federated/docs/source_zh_cn/conf.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import shutil
-import sys
-import IPython
-import re
-from sphinx.ext import autodoc as sphinx_autodoc
-
-# -- Project information -----------------------------------------------------
-
-project = 'MindSpore Federated'
-copyright = 'MindSpore'
-author = 'MindSpore'
-
-# The full version, including alpha/beta/rc tags
-release = 'master'
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-myst_enable_extensions = ["dollarmath", "amsmath"]
-
-
-myst_heading_anchors = 5
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.napoleon',
-    'sphinx.ext.viewcode',
-    'myst_parser',
-    'sphinx.ext.mathjax',
-    'IPython.sphinxext.ipython_console_highlighting'
-]
-
-source_suffix = {
-    '.rst': 'restructuredtext',
-    '.md': 'markdown',
-}
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-mathjax_path = 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/mathjax/MathJax-3.2.2/es5/tex-mml-chtml.js'
-
-mathjax_options = {
-    'async':'async'
-}
-
-smartquotes_action = 'De'
-
-exclude_patterns = []
-
-pygments_style = 'sphinx'
-
-# -- Options for HTML output -------------------------------------------------
-
-# Reconstruction of sphinx auto generated document translation.
-language = 'zh_CN'
-locale_dirs = ['../../../../resource/locale/']
-gettext_compact = False
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'sphinx_rtd_theme'
-
-html_search_language = 'zh'
-
-html_search_options = {'dict': '../../../resource/jieba.txt'}
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
-    'python': ('https://docs.python.org/3', '../../../../resource/python_objects.inv'),
-}
-
-from sphinx import directives
-with open('../_ext/overwriteobjectiondirective.txt', 'r', encoding="utf8") as f:
-    exec(f.read(), directives.__dict__)
-
-from sphinx.ext import viewcode
-with open('../_ext/overwriteviewcode.txt', 'r', encoding="utf8") as f:
-    exec(f.read(), viewcode.__dict__)
-
-# Modify default signatures for autodoc.
-autodoc_source_path = os.path.abspath(sphinx_autodoc.__file__)
-autodoc_source_re = re.compile(r'stringify_signature\(.*?\)')
-get_param_func_str = r"""\
-import re
-import inspect as inspect_
-
-def get_param_func(func):
-    try:
-        source_code = inspect_.getsource(func)
-        if func.__doc__:
-            source_code = source_code.replace(func.__doc__, '')
-        all_params_str = re.findall(r"def [\w_\d\-]+\(([\S\s]*?)(\):|\) ->.*?:)", source_code)
-        all_params = re.sub("(self|cls)(,|, )?", '', all_params_str[0][0].replace("\n", "").replace("'", "\""))
-        return all_params
-    except:
-        return ''
-
-def get_obj(obj):
-    if isinstance(obj, type):
-        return obj.__init__
-
-    return obj
-"""
-
-with open(autodoc_source_path, "r+", encoding="utf8") as f:
-    code_str = f.read()
-    code_str = autodoc_source_re.sub('"(" + get_param_func(get_obj(self.object)) + ")"', code_str, count=0)
-    exec(get_param_func_str, sphinx_autodoc.__dict__)
-    exec(code_str, sphinx_autodoc.__dict__)
-
-# Copy source files of chinese python api from federated repository.
-from sphinx.util import logging
-logger = logging.getLogger(__name__)
-
-copy_path = 'docs/api/api_python'
-src_dir = os.path.join(os.getenv("MF_PATH"), copy_path)
-
-copy_list = []
-
-present_path = os.path.dirname(__file__)
-
-for i in os.listdir(src_dir):
-    if os.path.isfile(os.path.join(src_dir,i)):
-        if os.path.exists('./'+i):
-            os.remove('./'+i)
-        shutil.copy(os.path.join(src_dir,i),'./'+i)
-        copy_list.append(os.path.join(present_path,i))
-    else:
-        if os.path.exists('./'+i):
-            shutil.rmtree('./'+i)
-        shutil.copytree(os.path.join(src_dir,i),'./'+i)
-        copy_list.append(os.path.join(present_path,i))
-
-# add view
-import json
-
-if os.path.exists('../../../../tools/generate_html/version.json'):
-    with open('../../../../tools/generate_html/version.json', 'r+', encoding='utf-8') as f:
-        version_inf = json.load(f)
-elif os.path.exists('../../../../tools/generate_html/daily_dev.json'):
-    with open('../../../../tools/generate_html/daily_dev.json', 'r+', encoding='utf-8') as f:
-        version_inf = json.load(f)
-elif os.path.exists('../../../../tools/generate_html/daily.json'):
-    with open('../../../../tools/generate_html/daily.json', 'r+', encoding='utf-8') as f:
-        version_inf = json.load(f)
-
-if os.getenv("MF_PATH").split('/')[-1]:
-    copy_repo = os.getenv("MF_PATH").split('/')[-1]
-else:
-    copy_repo = os.getenv("MF_PATH").split('/')[-2]
-
-branch = [version_inf[i]['branch'] for i in range(len(version_inf)) if version_inf[i]['name'] == copy_repo][0]
-docs_branch = [version_inf[i]['branch'] for i in range(len(version_inf)) if version_inf[i]['name'] == 'tutorials'][0]
-
-re_view = f"\n.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/{docs_branch}/" + \
-          f"resource/_static/logo_source.svg\n    :target: https://gitee.com/mindspore/{copy_repo}/blob/{branch}/"
-
-for cur, _, files in os.walk(present_path):
-    for i in files:
-        flag_copy = 0
-        if i.endswith('.rst'):
-            for j in copy_list:
-                if j in cur:
-                    flag_copy = 1
-                    break
-            if os.path.join(cur, i) in copy_list or flag_copy:
-                try:
-                    with open(os.path.join(cur, i), 'r+', encoding='utf-8') as f:
-                        content = f.read()
-                        new_content = content
-                        if '.. include::' in content and '.. automodule::' in content:
-                            continue
-                        if 'autosummary::' not in content and "\n=====" in content:
-                            re_view_ = re_view + copy_path + cur.split(present_path)[-1] + '/' + i + \
-                                       '\n    :alt: 查看源文件\n\n'
-                            new_content = re.sub('([=]{5,})\n', r'\1\n' + re_view_, content, 1)
-                        if new_content != content:
-                            f.seek(0)
-                            f.truncate()
-                            f.write(new_content)
-                except Exception:
-                    print(f'打开{i}文件失败')
-
-import mindspore_federated
-
-sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
-# import anchor_mod
-import nbsphinx_mod
-
-
-sys.path.append(os.path.abspath('../../../../resource/search'))
-import search_code
-
-sys.path.append(os.path.abspath('../../../../resource/custom_directives'))
-from custom_directives import IncludeCodeDirective
-
-def setup(app):
-    app.add_directive('includecode', IncludeCodeDirective)
-
-src_release = os.path.join(os.getenv("MF_PATH"), 'RELEASE_CN.md')
-des_release = "./RELEASE.md"
-with open(src_release, "r", encoding="utf-8") as f:
-    data = f.read()
-if len(re.findall("\n## (.*?)\n",data)) > 1:
-    content = re.findall("(## [\s\S\n]*?)\n## ", data)
-else:
-    content = re.findall("(## [\s\S\n]*)", data)
-#result = content[0].replace('# MindSpore', '#', 1)
-with open(des_release, "w", encoding="utf-8") as p:
-    p.write("# Release Notes"+"\n\n")
-    p.write(content[0])
diff --git a/docs/federated/docs/source_zh_cn/cross_device.rst b/docs/federated/docs/source_zh_cn/cross_device.rst
deleted file mode 100644
index 813aa7f74d..0000000000
--- a/docs/federated/docs/source_zh_cn/cross_device.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-端侧客户端
-============
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/cross_device.rst
-    :alt: 查看源文件
-
-.. toctree::
-   :maxdepth: 1
-
-   java_api_callback
-   java_api_client
-   java_api_clientmanager
-   java_api_dataset
-   java_api_flparameter
-   java_api_syncfljob
-   interface_description_federated_client
\ No newline at end of file
diff --git a/docs/federated/docs/source_zh_cn/data_join.md b/docs/federated/docs/source_zh_cn/data_join.md
deleted file mode 100644
index 8d9e3ba5e0..0000000000
--- a/docs/federated/docs/source_zh_cn/data_join.md
+++ /dev/null
@@ -1,242 +0,0 @@
-# 纵向联邦学习数据接入
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/data_join.md)
-
-和横向联邦学习不同，纵向联邦学习训练或推理时，两个参与方（leader和follower）拥有相同样本空间。因此，在纵向联邦学习的双方发起训练或推理之前，必须协同完成数据求交。双方必须读取各自的原始数据，并提取出每条数据对应的ID（每条数据的唯一标识符，且都不相同）进行求交（即求取交集）。然后，双方根据求交后的ID从原始数据中获得特征或标签等数据。最后各自导出持久化文件，并在后续训练或推理之前保序地读取数据。
-
-## 总体流程
-
-数据接入可以分为数据导出和数据读取两个部分。
-
-### 数据导出
-
-MindSpore Federated纵向联邦学习数据导出流程框架如图1所示：
-
-![](./images/data_join.png)
-
-图 1 纵向联邦学习数据接入流程框架图
-
-在数据导出流程中，Leader Worker和 Follower Worker为纵向联邦学习的两个参与方。Leader Worker常驻并保持对Follower Worker的监听，Follower Worker可以在任意时刻进入数据接入流程中。
-
-在Leader Worker收到 Follower Worker的注册请求后，会对注册内容进行校验。若注册成功，则给Follower Worker发送任务相关的超参（PSI 相关超参、分桶规则、ID字段名称等）。
-
-然后Leader Worker 和 Follower Worker 分别读取各自的原始数据，再从各自的原始数据中提取出 ID 列表并实现分桶。
-
-Leader Worker 和 Follower Worker 的每个桶都启动隐私求交方法获得两方的ID交集。
-
-最后，两方根据ID交集提取原始数据中相应的数据并导出成MindRecord格式的文件。
-
-### 数据读取
-
-纵向联邦要求两个参与方在训练或推理的每一个批次的数据ID的值和顺序都一样的。MindSpore Federated通过在两方读取各自数据时，使用相同的随机种子和对导出的文件集合使用字典排序的方法，保证数据读取的顺序一致。
-
-## 快速体验
-
-### 数据准备样例
-
-若要使用数据接入方法，首先需要准备好原始数据。用户可以使用[随机数据生成脚本](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/generate_random_data.py)生成出各参与方的伪造数据作为样例。
-
-```shell
-python generate_random_data.py \
-    --seed=0 \
-    --total_output_path=vfl/input/total_data.csv \
-    --intersection_output_path=vfl/input/intersection_data.csv \
-    --leader_output_path=vfl/input/leader_data_*.csv \
-    --follower_output_path=vfl/input/follower_data_*.csv \
-    --leader_file_num=4 \
-    --follower_file_num=2 \
-    --leader_data_num=300 \
-    --follower_data_num=200 \
-    --overlap_num=100 \
-    --id_len=20 \
-    --feature_num=30
-```
-
-用户可根据实际情况进行超参设置：
-
-| 超参名称             | 超参描述                                                     |
-| -------------------- | ------------------------------------------------------------ |
-| seed                 | 随机种子，int类型。                                          |
-| total_output_path    | 所有数据的输出路径，str类型。                                |
-| intersection_output_path    | 交集数据的输出路径，str类型。                                |
-| leader_output_path   | leader方数据的输出路径。若配置的内容包括`*`号，则会在导出多个文件时将`*`号依次替换为0、1、2……的序号。str类型。 |
-| follower_output_path | follower方数据的输出路径。若配置的内容包括`*`号，则会在导出多个文件时将`*`号依次替换为0、1、2……的序号。str类型。 |
-| leader_file_num      | leader方数据的输出文件数目，int类型。                        |
-| follower_file_num    | follower方数据的输出文件数目，int类型。                      |
-| leader_data_num      | leader方数据总量，int类型。                                  |
-| follower_data_num    | follower方数据总量，int类型。                                |
-| overlap_num          | 两方重叠的数据总量，int类型。                                |
-| id_len               | 数据ID为字符串类型。该超参为字符串的长度，int类型。          |
-| feature_num          | 导出的数据的列数。                                           |
-
-运行数据准备后生成多个csv文件：
-
-```text
-follower_data_0.csv
-follower_data_1.csv
-intersection_data.csv
-leader_data_0.csv
-leader_data_1.csv
-leader_data_2.csv
-leader_data_3.csv
-```
-
-### 数据导出样例
-
-用户可以使用[数据求交脚本](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/run_data_join.py)实现两方数据求交并导出成MindRecord格式文件。用户需要分别启动Leader和Follower两个进程。
-
-启动Leader：
-
-```shell
-python run_data_join.py \
-    --role="leader" \
-    --main_table_files="vfl/input/leader/" \
-    --output_dir="vfl/output/leader/" \
-    --data_schema_path="vfl/leader_schema.yaml" \
-    --server_name=leader_node \
-    --http_server_address="127.0.0.1:1086" \
-    --remote_server_name=follower_node \
-    --remote_server_address="127.0.0.1:1087" \
-    --primary_key="oaid" \
-    --bucket_num=5 \
-    --store_type="csv" \
-    --shard_num=1 \
-    --join_type="psi" \
-    --thread_num=0
-```
-
-启动Follower：
-
-```shell
-python run_data_join.py \
-    --role="follower" \
-    --main_table_files="vfl/input/follower/" \
-    --output_dir="vfl/output/follower/" \
-    --data_schema_path="vfl/follower_schema.yaml" \
-    --server_name=follower_node \
-    --http_server_address="127.0.0.1:1087" \
-    --remote_server_name=leader_node \
-    --remote_server_address="127.0.0.1:1086" \
-    --store_type="csv" \
-    --thread_num=0
-```
-
-用户可根据实际情况进行超参设置：
-
-| 超参名称            | 超参描述                                                |
-| ------------------- | ------------------------------------------------------- |
-| role                | worker的角色类型，str类型，包括："leader"、"follower"。 |
-| main_table_files                  | 原始数据路径，可以配置单个或多个文件路径、数据目录路径，list或str类型。 |
-| output_dir                        | 导出的MindRecord相关文件的目录路径，str类型。                |
-| data_schema_path         | 导出时所需要配置的超参文件存放的路径，str类型。         |
-| server_name      |本地用于通信的http服务名字，str类型。                             |
-| http_server_address      | 本机IP和端口地址，str类型。                             |
-| remote_server_name | 对端用于通信的http服务名字，str类型。                                |
-| remote_server_address | 对端IP和端口地址，str类型。                                |
-| primary_key（Follower不需要配置） | 数据ID的名称，str类型。                                      |
-| bucket_num（Follower不需要配置）  | 求交和导出时，分桶的数目，int类型。                          |
-| store_type                        | 原始数据存储类型，str类型。包括："csv"。                                  |
-| shard_num（Follower不需要配置）   | 单个桶导出的文件数量，int类型。                              |
-| join_type（Follower不需要配置）   | 求交算法，str类型。包括："psi"。                                          |
-| thread_num                        | 使用PSI求交算法时，计算所需线程数，int类型。                 |
-
-在上述样例中，data_schema_path对应的文件可以参考[leader_schema.yaml](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/vfl/leader_schema.yaml)和[follower_schema.yaml](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/vfl/follower_schema.yaml)中的相应文件配置。用户需要在该文件中提供要导出的数据的列名和类型。
-
-运行数据导出后生成多个MindRecord相关文件：
-
-```text
-mindrecord_0
-mindrecord_0.db
-mindrecord_1
-mindrecord_1.db
-mindrecord_2
-mindrecord_2.db
-mindrecord_3
-mindrecord_3.db
-mindrecord_4
-mindrecord_4.db
-```
-
-### 数据读取样例
-
-用户可以使用[读取数据脚本](https://gitee.com/mindspore/federated/blob/master/tests/st/data_join/load_joined_data.py)实现求交后的数据读取：
-
-```shell
-python load_joined_data.py \
-    --seed=0 \
-    --input_dir=vfl/output/leader/ \
-    --shuffle=True
-```
-
-用户可根据实际情况进行超参设置：
-
-| 超参名称  | 超参描述                                  |
-| --------- | ----------------------------------------- |
-| seed      | 随机种子，int类型。                       |
-| input_dir | 输入的MindRecord相关文件的目录，str类型。 |
-| shuffle   | 数据是否需要打乱，bool类型。              |
-
-如果求交结果正确，两方各自读取数据时，两方的每条数据的OAID顺序一致，而每条数据中的其他列的数据可以为不同值。运行数据读取后打印交集数据：
-
-```text
-Leader数据导出运行结果：
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'uMbgxIMMwWhMGrVMVtM7')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'IwoGP08kWVtT4WHL2PLu')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'MSRe6mURtxgyEgWzDn0b')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'y7X0WcMKnTLrhxVcWfGF')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'DicKRIVvbOYSiv63TvcL')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'TCHgtynOhH3z11QYemsH')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'OWmhgIfC3k8UTteGUhni')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'NTV3qEYXBHqKBWyHGc7s')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'wuinSeN1bzYgXy4XmSlR')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'SSsCU0Pb46XGzUIa3Erg')}
-……
-
-Follower数据导出运行结果：
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'uMbgxIMMwWhMGrVMVtM7')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'IwoGP08kWVtT4WHL2PLu')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'MSRe6mURtxgyEgWzDn0b')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'y7X0WcMKnTLrhxVcWfGF')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'DicKRIVvbOYSiv63TvcL')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'TCHgtynOhH3z11QYemsH')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'OWmhgIfC3k8UTteGUhni')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'NTV3qEYXBHqKBWyHGc7s')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'wuinSeN1bzYgXy4XmSlR')}
-{……, 'oaid': Tensor(shape=[], dtype=String, value= 'SSsCU0Pb46XGzUIa3Erg')}
-……
-```
-
-## 深度体验
-
-下列代码的详细的API文档可以参考[数据接入文档](https://www.mindspore.cn/federated/docs/zh-CN/master/data_join/data_join.html)。
-
-### 数据导出
-
-用户可以使用已经封装好的接口和配置文件实现数据求交以及导出MindRecord相关文件，方法如下：
-
-```python
-from mindspore_federated import FLDataWorker
-from mindspore_federated.common.config import get_config
-
-
-if __name__ == '__main__':
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    args = get_config(os.path.join(current_dir, "vfl/vfl_data_join_config.yaml"))
-    dict_cfg = args.__dict__
-
-    worker = FLDataWorker(config=dict_cfg)
-    worker.do_worker()
-```
-
-### 数据读取
-
-用户可以使用已经封装好的接口实现导出的MindRecord相关文件的数据读取，方法如下：
-
-```python
-from mindspore_federated.data_join import load_mindrecord
-
-
-if __name__ == "__main__":
-    dataset = load_mindrecord(input_dir="vfl/output/leader/", shuffle=True, seed=0)
-```
-
diff --git a/docs/federated/docs/source_zh_cn/deploy_federated_client.md b/docs/federated/docs/source_zh_cn/deploy_federated_client.md
deleted file mode 100644
index a7b4e8ffb6..0000000000
--- a/docs/federated/docs/source_zh_cn/deploy_federated_client.md
+++ /dev/null
@@ -1,200 +0,0 @@
-# 横向联邦端侧部署
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/deploy_federated_client.md)
-
-本文档介绍如何编译，部署Federated-Client。
-
-## Linux 编译指导
-
-### 系统环境和第三方依赖
-
-本章节介绍如何完成MindSpore联邦学习的端侧编译，当前联邦学习端侧仅提供Linux上的编译指导，其他系统暂不支持。下表列出了编译所需的系统环境和第三方依赖。
-
-| 软件名称                  | 版本  |  作用 |
-|-----------------------| ------------ | ------------ |
-| Ubuntu                | 18.04.02LTS   | 编译和运行MindSpore的操作系统  |
-| [GCC](#安装gcc)         | 7.3.0到9.4.0之间  | 用于编译MindSpore的C++编译器 |
-| [git](#安装git)         | -  | MindSpore使用的源代码管理工具 |
-| [CMake](#安装cmake)     | 3.18.3及以上  | 编译构建MindSpore的工具 |
-| [Gradle](#安装gradle)   | 6.6.1  | 基于JVM的构建工具  |
-| [Maven](#安装maven)     | 3.3.1及以上  | Java项目的管理和构建工具  |
-| [OpenJDK](#安装openjdk) | 1.8 到 1.15之间  | Java项目的管理和构建工具  |
-
-#### 安装GCC
-
-可以通过以下命令安装GCC。
-
-```bash
-sudo apt-get install gcc-7 git -y
-```
-
-如果要安装更高版本的GCC，使用以下命令安装GCC 8。
-
-```bash
-sudo apt-get install gcc-8 -y
-```
-
-或者安装GCC 9。
-
-```bash
-sudo apt-get install software-properties-common -y
-sudo add-apt-repository ppa:ubuntu-toolchain-r/test
-sudo apt-get update
-sudo apt-get install gcc-9 -y
-```
-
-#### 安装git
-
-可以通过以下命令安装git。
-
-```bash
-sudo apt-get install git -y
-```
-
-#### 安装CMake
-
-可以通过以下命令安装[CMake](https://cmake.org/)。
-
-```bash
-wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | sudo apt-key add -
-sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(lsb_release -cs) main"
-sudo apt-get install cmake -y
-```
-
-#### 安装Gradle
-
-可以通过以下命令安装[Gradle](https://gradle.org/releases/)。
-
-```bash
-# 下载对应的压缩包，解压。
-# 配置环境变量：
-  export GRADLE_HOME=GRADLE路径
-  export GRADLE_USER_HOME=GRADLE路径
-# 将bin目录添加到PATH中：
-  export PATH=${GRADLE_HOME}/bin:$PATH
-```
-
-#### 安装Maven
-
-可以通过以下命令安装[Maven](https://archive.apache.org/dist/maven/maven-3/)。
-
-```bash
-# 下载对应的压缩包，解压。
-# 配置环境变量：
-  export MAVEN_HOME=MAVEN路径
-# 将bin目录添加到PATH中：
-  export PATH=${MAVEN_HOME}/bin:$PATH
-```
-
-#### 安装OpenJDK
-
-可以通过以下命令安装[OpenJDK](https://jdk.java.net/archive/)。
-
-```bash
-# 下载对应的压缩包，解压。
-# 配置环境变量：
-  export JAVA_HOME=JDK路径
-# 将bin目录添加到PATH中：
-  export PATH=${JAVA_HOME}/bin:$PATH
-```
-
-### 验证是否成功安装
-
-确认[系统环境和第三方依赖](#系统环境和第三方依赖)中安装是否成功。
-
-```text
-打开命令窗口数输入：gcc --version
-输出以下结果标识安装成功：
-  gcc version 版本号
-
-打开命令窗口数输入：git --version
-输出以下结果标识安装成功：
-  git version 版本号
-
-打开命令窗口数输入：cmake --version
-输出以下结果标识安装成功：
-  cmake version 版本号
-
-打开命令窗口数输入：gradle --version
-输出以下结果标识安装成功：
-  Gradle 版本号
-
-打开命令窗口数输入：mvn --version
-输出以下结果标识安装成功：
-  Apache Maven 版本号
-
-打开命令窗口数输入：java --version
-输出以下结果标识安装成功：
-  openjdk version 版本号
-
-```
-
-### 编译选项
-
-联邦学习device_client目录下的`cli_build.sh`脚本用于联邦学习端侧的编译。
-
-#### cli_build.sh的参数使用说明
-
-| 参数 | 参数说明                 | 取值范围 | 默认值       |
-| ---- | ------------------------ | -------- | ------------ |
-| -p   | 依赖外部包的下载存放路径 | 字符串   | third |
-| -c   | 是否复用之前下载的依赖包 | on、off  | on           |
-
-### 编译示例
-
-1. 首先，在进行编译之前，需从gitee代码仓下载源码。
-
-    ```bash
-    git clone https://gitee.com/mindspore/federated.git ./
-    ```
-
-2. 然后进入目录mindspore_federated/device_client，执行如下命令：
-
-    ```bash
-    bash cli_build.sh
-    ```
-
-3. 由于端侧框架和模型是解耦的，我们提供的x86架构包mindspore-lite-{version}-linux-x64.tar.gz不包含模型相关脚本，因此需要用户自行生成模型脚本对应的jar包，我们提供的模型脚本对应jar包可采用如下方式获取：
-
-    ```bash
-    cd federated/example/quick_start_flclient
-    bash build.sh -r mindspore-lite-java-flclient.jar #-r 后需要给出最新x86架构包绝对路径(步骤2生成，federated/mindspore_federated/device_client/build/libs/jarX86/mindspore-lite-java-flclient.jar)
-    ```
-
-运行以上指令后生成jar包路径为：federated/example/quick_start_flclient/target/quick_start_flclient.jar。
-
-### 构建依赖环境
-
-1. 将文件`federated/mindspore_federated/device_client/third/mindspore-lite-{version}-linux-x64.tar.gz`解压后，所得到的目录结构如下所示(联邦学习l不使用的文件不展示)：
-
-    ```sh
-    mindspore-lite-{version}-linux-x64
-    ├── tools
-    └── runtime
-        ├── include  # 训练框架头文件
-        ├── lib      # 训练框架库
-        │   ├── libminddata-lite.a          # 图像处理静态库文件
-        │   ├── libminddata-lite.so        # 图像处理动态库文件
-        │   ├── libmindspore-lite-jni.so   # MindSpore Lite推理框架依赖的jni动态库
-        │   ├── libmindspore-lite-train.a  # MindSpore Lite训练框架依赖的静态库
-        │   ├── libmindspore-lite-train.so # MindSpore Lite训练框架依赖的动态库
-        │   ├── libmindspore-lite-train-jni.so # MindSpore Lite训练框架依赖的jni动态库
-        │   ├── libmindspore-lite.a  # MindSpore Lite推理框架依赖的静态库
-        │   ├── libmindspore-lite.so  # MindSpore Lite推理框架依赖的动态库
-        │   └── mindspore-lite-java.jar    # MindSpore Lite训练框架jar包
-        └── third_party
-            ├── glog
-            │└── libmindspore_glog.so.0   #glog 日志动态库文件
-            └── libjpeg-turbo
-                └── lib
-                    ├── libjpeg.so.62   # 图像处理动态库文件
-                    └── libturbojpeg.so.0  # 图像处理动态库文件
-    ```
-
-2. 可将路径`mindspore-lite-{version}-linux-x64/runtime/lib/`、`mindspore-lite-{version}-linux-x64/runtime/third_party/glog/`以及`mindspore-lite-{version}-linux-x64/runtime/third_party/libjpeg-turbo/lib/`中联邦学习所依赖的so文件放入一个文件夹，比如`/resource/x86libs/`。然后在x86中设置环境变量(下面需提供绝对路径)：
-
-    ```sh
-    export LD_LIBRARY_PATH=/resource/x86libs/:$LD_LIBRARY_PATH
-    ```
-
-3. 设置好依赖环境之后，可参考应用实践教程[实现一个端云联邦的图像分类应用(x86)](https://www.mindspore.cn/federated/docs/zh-CN/master/image_classification_application.html)在x86环境中模拟启动多个客户端进行联邦学习。
\ No newline at end of file
diff --git a/docs/federated/docs/source_zh_cn/deploy_federated_server.md b/docs/federated/docs/source_zh_cn/deploy_federated_server.md
deleted file mode 100644
index 317e44ece7..0000000000
--- a/docs/federated/docs/source_zh_cn/deploy_federated_server.md
+++ /dev/null
@@ -1,317 +0,0 @@
-# 横向联邦云侧部署
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/deploy_federated_server.md)
-
-本文档以LeNet网络为例，讲解如何使用MindSpore Federated部署横向联邦学习集群。
-
-MindSpore Federated Learning (FL) Server集群物理架构如图所示：
-
-![](./images/mindspore_federated_networking.png)
-
-如上图所示，在横向联邦学习云侧集群中，有三种角色的MindSpore进程：`Federated Learning Scheduler`、`Federated Learning Server`和`Federated Learning Worker`：
-
-- Federated Learning Scheduler
-
-    `Scheduler`的功能主要包括：
-
-    1. 协助集群组网：在集群初始化阶段，由`Scheduler`负责收集`Server`信息，并保障集群一致性。
-    2. 开放管理面：向用户提供`RESTful`接口，实现对集群的管理。
-
-    在一个联邦学习任务中，只有一个`Scheduler`，其与`Server`通过TCP协议通信。
-
-- Federated Learning Server
-
-    `Server`为执行联邦学习任务的主体，用于接收和解析端侧设备上传的数据，具有执行安全聚合、限时通信、模型存储等能力。在一个联邦学习任务中，`Server`可以有多个(用户可配置)，`Server`间通过TCP协议通信，对外开放HTTP端口与端侧设备连接。
-
-    在MindSpore联邦学习框架中，`Server`还支持弹性伸缩以及容灾，能够在训练任务不中断的情况下，动态调配硬件资源。
-
-- Federated Learning Worker
-
-    `Worker`为执行联邦学习任务的附件模块，用于对Server中的模型进行二次有监督训练，而后将训练所得模型下发给Server，在一个联邦学习任务中，`Worker`可以有多个(用户可配置)，`Worker`和`Server`间通过TCP协议通信。
-
-`Scheduler`和`Server`需部署在单网卡的服务器或者容器中，且处于相同网段。MindSpore自动获取首个可用IP地址作为`Server`地址。
-
-> 服务器会校验客户端携带的时间戳，需要确保服务器定期同步时钟，避免服务器出现较大的时钟偏移。
-
-## 准备环节
-
-> 建议使用[Anaconda](https://www.anaconda.com/)创建虚拟环境进行如下操作。
-
-### 安装MindSpore
-
-MindSpore横向联邦学习云侧集群支持在x86 CPU和GPU CUDA硬件平台上部署。可参考[MindSpore安装指南](https://www.mindspore.cn/install)安装MindSpore最新版本。
-
-### 安装MindSpore Federated
-
-通过[源码](https://gitee.com/mindspore/federated)编译安装。
-
-```shell
-git clone https://gitee.com/mindspore/federated.git -b master
-cd federated
-bash build.sh
-```
-
-对于`bash build.sh`，可通过例如`-jn`选项，例如`-j16`，加速编译；可通过`-S on`选项，从gitee而不是github下载第三方依赖。
-
-编译完成后，在`build/package/`目录下找到Federated的whl安装包进行安装：
-
-```bash
-pip install mindspore_federated-{version}-{python_version}-linux_{arch}.whl
-```
-
-### 验证是否成功安装
-
-执行以下命令，验证安装结果。导入Python模块不报错即安装成功：
-
-```python
-from mindspore_federated import FLServerJob
-```
-
-### 安装和启动Redis服务器
-
-联邦学习默认依赖[Redis服务器](https://gitee.com/link?target=https%3A%2F%2Fredis.io%2F)作为缓存数据中间件，运行联邦学习业务，需要安装和运行Redis服务器。
-
-> 用户需自行检查Redis版本的安全性，某些Redis版本可能存在漏洞。
-
-安装Redis服务器：
-
-```bash
-sudo apt-get install redis
-```
-
-运行Redis服务器，配置端口号为：23456：
-
-```bash
-redis-server --port 23456 --save ""
-```
-
-## 启动集群
-
-1. [样例路径](https://gitee.com/mindspore/federated/tree/master/example/cross_device_lenet_femnist/)。
-
-    ```bash
-    cd example/cross_device_lenet_femnist
-    ```
-
-2. 据实际运行需要修改yaml配置文件：`default_yaml_config.yaml`，如下为[Lenet的相关配置样例](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/yamls/lenet/default_yaml_config.yaml)。
-
-    ```yaml
-    fl_name: Lenet
-    fl_iteration_num: 25
-    server_mode: FEDERATED_LEARNING
-    enable_ssl: False
-
-    distributed_cache:
-      type: redis
-      address: 127.0.0.1:23456 # ip:port of redis actual machine
-      plugin_lib_path: ""
-
-    round:
-      start_fl_job_threshold: 2
-      start_fl_job_time_window: 30000
-      update_model_ratio: 1.0
-      update_model_time_window: 30000
-      global_iteration_time_window: 60000
-
-    summary:
-      metrics_file: "metrics.json"
-      failure_event_file: "event.txt"
-      continuous_failure_times: 10
-      data_rate_dir: ".."
-      participation_time_level: "5,15"
-
-    unsupervised:
-      cluster_client_num: 1000
-      eval_type: SILHOUETTE_SCORE
-
-    encrypt:
-      encrypt_train_type: NOT_ENCRYPT
-      pw_encrypt:
-        share_secrets_ratio: 1.0
-        cipher_time_window: 3000
-        reconstruct_secrets_threshold: 1
-      dp_encrypt:
-        dp_eps: 50.0
-        dp_delta: 0.01
-        dp_norm_clip: 1.0
-      signds:
-        sign_k: 0.01
-        sign_eps: 100
-        sign_thr_ratio: 0.6
-        sign_global_lr: 0.1
-        sign_dim_out: 0
-
-    compression:
-      upload_compress_type: NO_COMPRESS
-      upload_sparse_rate: 0.4
-      download_compress_type: NO_COMPRESS
-
-    ssl:
-      # when ssl_config is set
-      # for tcp/http server
-      server_cert_path: "server.p12"
-      # for tcp client
-      client_cert_path: "client.p12"
-      # common
-      ca_cert_path: "ca.crt"
-      crl_path: ""
-      cipher_list: "ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-CHACHA20-POLY1305:ECDHE-PSK-CHACHA20-POLY1305:ECDHE-ECDSA-AES128-CCM:ECDHE-ECDSA-AES256-CCM:ECDHE-ECDSA-CHACHA20-POLY1305"
-      cert_expire_warning_time_in_day: 90
-
-    client_verify:
-      pki_verify: false
-      root_first_ca_path: ""
-      root_second_ca_path: ""
-      equip_crl_path: ""
-      replay_attack_time_diff: 600000
-
-    client:
-      http_url_prefix: ""
-      client_epoch_num: 20
-      client_batch_size: 32
-      client_learning_rate: 0.01
-      connection_num: 10000
-
-    ```
-
-3. 准备模型文件，启动方式为：基于权重启动，需要提供相应的模型权重。
-
-    获取lenet模型权重：
-
-    ```bash
-    wget https://ms-release.obs.cn-north-4.myhuaweicloud.com/ms-dependencies/Lenet.ckpt
-    ```
-
-4. 运行Scheduler，管理面地址默认为`127.0.0.1:11202`。
-
-    ```python
-    python run_sched.py \
-    --yaml_config="yamls/lenet.yaml" \
-    --scheduler_manage_address="10.*.*.*:18019"
-    ```
-
-5. 运行Server，默认启动1个Server，HTTP服务器地址默认为`127.0.0.1:6666`。
-
-    ```python
-    python run_server.py \
-    --yaml_config="yamls/lenet.yaml" \
-    --tcp_server_ip="10.*.*.*" \
-    --checkpoint_dir="fl_ckpt" \
-    --local_server_num=1 \
-    --http_server_address="10.*.*.*:8019"
-    ```
-
-6. 停止联邦学习。当前版本联邦学习集群为常驻进程，可执行`finish_cloud.py`脚本，以终止联邦学习服务。执行指令的示例如下，其中`redis_port`传参，需与启动redis时的传参保持一致，代表停止此`Scheduler`对应的集群。
-
-    ```python
-    python finish_cloud.py --redis_port=23456
-    ```
-
-    若console打印如下内容：
-
-    ```text
-    killed $PID1
-    killed $PID2
-    killed $PID3
-    killed $PID4
-    killed $PID5
-    killed $PID6
-    killed $PID7
-    killed $PID8
-    ```
-
-    则表明停止服务成功。
-
-## 弹性伸缩
-
-MindSpore联邦学习框架支持`Server`的弹性伸缩，对外通过`Scheduler`管理端口提供`RESTful`服务，使得用户在不中断训练任务的情况下，对硬件资源进行动态调度。
-
-以下示例介绍了如何通过对应接口，对控制集群扩容/缩容。
-
-### 扩容
-
-在集群启动后，进入部署scheduler节点的机器，向`Scheduler`发起请求，查询状态、节点信息。可使用`curl`指令构造`RESTful`请求。
-
-```sh
-curl -k 'http://10.*.*.*:18015/state'
-```
-
-`Scheduler`将返回`json`格式的查询结果。
-
-```json
-{
-  "message":"Get cluster state successful.",
-  "cluster_state":"CLUSTER_READY",
-  "code":0,
-  "nodes":[
-    {"node_id","{ip}:{port}::{timestamp}::{random}",
-     "tcp_address":"{ip}:{port}",
-     "role":"SERVER"}
-  ]
-}
-```
-
-需要拉起3个新的`Server`进程，并将`local_server_num`参数累加扩容的个数，从而保证全局组网信息的正确性，即扩容后，`local_server_num`的数量应为4，执行指令的示例如下：
-
-```sh
-python run_server.py --yaml_config="yamls/lenet.yaml" --tcp_server_ip="10.*.*.*" --checkpoint_dir="fl_ckpt" --local_server_num=4 --http_server_address="10.*.*.*:18015"
-```
-
-该指令代表启动四个`Server`节点，总`Server`数量为4。
-
-### 缩容
-
-直接使用kill -9 pid的方式模拟缩容，使用`curl`指令构造`RESTful`请求，查询状态，发现集群中少了一个node_id，达到缩容目的。
-
-```sh
-curl -k \
-'http://10.*.*.*:18015/state'
-```
-
-`Scheduler`将返回`json`格式的查询结果。
-
-```json
-{
-  "message":"Get cluster state successful.",
-  "cluster_state":"CLUSTER_READY",
-  "code":0,
-  "nodes":[
-    {"node_id","{ip}:{port}::{timestamp}::{random}",
-     "tcp_address":"{ip}:{port}",
-     "role":"SERVER"},
-    {"node_id","worker_fl_{timestamp}::{random}",
-     "tcp_address":"",
-     "role":"WORKER"}，
-    {"node_id","worker_fl_{timestamp}::{random}",
-     "tcp_address":"",
-     "role":"WORKER"}
-  ]
-}
-```
-
-> - 在集群扩容/缩容成功后，训练任务会自动恢复，不需要用户进行额外干预。
-
-## 安全
-
-MindSpore联邦学习框架支持`Server`的SSL安全认证，要开启安全认证，需要在启动命令加上`enable_ssl=True`，config_file_path指定的config.json配置文件需要添加如下字段：
-
-```json
-{
-    "server_cert_path": "server.p12",
-    "crl_path": "",
-    "client_cert_path": "client.p12",
-    "ca_cert_path": "ca.crt",
-    "cert_expire_warning_time_in_day": 90,
-    "cipher_list": "ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:kEDH+AESGCM:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES256-SHA:DHE-RSA-AES128-SHA256:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA:DHE-RSA-AES256-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!3DES:!MD5:!PSK",
-    "connection_num":10000
-}
-```
-
-- server_cert_path：服务端包含证书和密钥的密文的p12文件路径。
-- crl_path：吊销列表的文件。
-- client_cert_path：客户端包含证书和密钥的密文的p12文件路径。
-- ca_cert_path：根证书。
-- cipher_list：密码套件。
-- cert_expire_warning_time_in_day：证书过期的告警时间。
-
-p12文件中的密钥为密文存储。
diff --git a/docs/federated/docs/source_zh_cn/deploy_vfl.md b/docs/federated/docs/source_zh_cn/deploy_vfl.md
deleted file mode 100644
index 640c22f706..0000000000
--- a/docs/federated/docs/source_zh_cn/deploy_vfl.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# 纵向联邦部署
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/deploy_vfl.md)
-
-本文档讲解如何使用和部署纵向联邦学习框架。
-
-MindSpore Vertical Federated Learning (VFL) 物理架构如图所示：
-
-![](./images/deploy_VFL.png)
-
-如上图所示，在纵向联邦的交互中有两个参与方：Leader node和Follower node，每一个参与方都有两种角色的进程：`FLDataWorker`和`VFLTrainer`：
-
-- FLDataWorker
-
-    `FLDataWorker`的功能主要包括：
-
-    1. 数据集合求交：获得纵向联邦参与双方的共有用户交集，支持隐私集合求交协议，可防止联邦学习参与方获得交集外的ID信息。
-    2. 训练数据生成：在获得交集ID之后，扩充数据特征，生成用于训练的mindrecord文件。
-    3. 开放管理面：向用户提供`RESTful`接口，实现对集群的管理。
-
-    在一个联邦学习任务中，只有一个`Scheduler`，其与`Server`通过TCP协议通信。
-
-- VFLTrainer
-
-    `VFLTrainer`为执行纵向联邦训练任务的主体，执行模型拆分后的正反向计算、Embedding张量传输，梯度张量传输、反向优化器更新等任务。当前版本支持单机单卡和单机多卡的训练模式。
-
-    在MindSpore联邦学习框架中，`Server`还支持弹性伸缩以及容灾，能够在训练任务不中断的情况下，动态调配硬件资源。
-
-`FLDataWorker`和`VFLTrainer`一般部署在同一台服务器或者容器中。
-
-## 准备环节
-
-> 建议使用[Anaconda](https://www.anaconda.com/)创建虚拟环境进行如下操作。
-
-### 安装MindSpore
-
-MindSpore纵向联邦支持在x86 CPU、GPU CUDA和Ascend硬件平台上部署。可参考[MindSpore安装指南](https://www.mindspore.cn/install)安装MindSpore最新版本。
-
-### 安装MindSpore Federated
-
-通过[源码](https://gitee.com/mindspore/federated)编译安装。
-
-```shell
-git clone https://gitee.com/mindspore/federated.git -b master
-cd federated
-bash build.sh
-```
-
-对于`bash build.sh`，可通过例如`-jn`选项，例如`-j16`，加速编译；可通过`-S on`选项，从gitee而不是github下载第三方依赖。
-
-编译完成后，在`build/package/`目录下找到Federated的whl安装包进行安装：
-
-```shell
-pip install mindspore_federated-{version}-{python_version}-linux_{arch}.whl
-```
-
-#### 验证是否成功安装
-
-执行以下命令，验证安装结果。导入Python模块不报错即安装成功：
-
-```python
-from mindspore_federated import FLServerJob
-```
-
-## 运行样例
-
-FLDataWorker的运行样例可参考[纵向联邦学习数据接入](https://www.mindspore.cn/federated/docs/zh-CN/master/data_join.html)。
-
-VFLTrainer的运行样例可参考[纵向联邦学习模型训练 - Wide&Deep推荐应用](https://www.mindspore.cn/federated/docs/zh-CN/master/split_wnd_application.html)。
diff --git a/docs/federated/docs/source_zh_cn/faq.md b/docs/federated/docs/source_zh_cn/faq.md
deleted file mode 100644
index 7e9f84e777..0000000000
--- a/docs/federated/docs/source_zh_cn/faq.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# FAQ
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/faq.md)
-
-**Q: 请问如果集群组网不成功，怎么定位原因？**
-
-A: 请查看服务器的网络情况，譬如请查看防火墙是否禁止端口访问，请设置防火墙允许端口访问。
-
-
    
diff --git a/docs/federated/docs/source_zh_cn/federated_install.md b/docs/federated/docs/source_zh_cn/federated_install.md
deleted file mode 100644
index 7de6d374ee..0000000000
--- a/docs/federated/docs/source_zh_cn/federated_install.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# 获取MindSpore Federated
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/federated_install.md)
-
-[MindSpore Federated](https://gitee.com/mindspore/federated)框架代码现已独立建仓，分为端侧和云侧，其云侧能力依赖MindSpore和MindSpore Federated，利用MindSpore进行云侧集群聚合训练以及与端侧进行通信，因此需要分别获取MindSpore whl包和MindSpore Federated whl包。端侧能力依赖MindSpore Lite和MindSpore Federated java包，其中MindSpore Federated java主要负责数据预处理、调用MindSpore Lite进行模型训练和推理以及利用隐私保护机制和云侧进行模型相关的上传和下载。
-
-## 获取MindSpore whl包
-
-包括源码编译和下载发布版两种方式，支持x86 CPU、GPU CUDA等硬件平台，根据硬件平台类型，选择进行安装即可。安装步骤可参考[MindSpore安装指南](https://www.mindspore.cn/install)。
-
-## 获取MindSpore Lite java包
-
-包括源码编译和下载发布版两种方式。目前，MindSpore Lite联邦学习功能只支持Linux和Android平台，且只支持CPU。安装步骤可参考[下载MindSpore Lite](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)和[编译MindSpore Lite](https://www.mindspore.cn/lite/docs/zh-CN/master/build/build.html)。
-
-## 获取MindSpore Federated whl包
-
-包括源码编译和下载发布版两种方式，支持x86 CPU、GPU CUDA等硬件平台，根据硬件平台类型，选择进行安装即可。安装步骤可参考[编译MindSpore Federated whl](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_server.html)。
-
-## 获取MindSpore Federated java包
-
-包括源码编译和下载发布版两种方式。目前，MindSpore Federated联邦学习功能只支持Linux和Android平台。安装步骤可参考和[编译MindSpore Federated java](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)。
-
-## Linux编译环境要求
-
-目前源码编译只支持Linux，编译环境要求可参考[MindSpore源码编译](https://www.mindspore.cn/install)和[MindSpore Lite源码编译](https://www.mindspore.cn/lite/docs/zh-CN/master/build/build.html)。
diff --git a/docs/federated/docs/source_zh_cn/horizontal_server.rst b/docs/federated/docs/source_zh_cn/horizontal_server.rst
deleted file mode 100644
index d5a4319b56..0000000000
--- a/docs/federated/docs/source_zh_cn/horizontal_server.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-联邦服务器
-==============
-
-.. image:: https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg
-    :target: https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/horizontal_server.rst
-    :alt: 查看源文件
-
-.. toctree::
-   :maxdepth: 1
-
-   horizontal/federated_server
-   horizontal/federated_server_yaml
diff --git a/docs/federated/docs/source_zh_cn/image_classfication_dataset_process.md b/docs/federated/docs/source_zh_cn/image_classfication_dataset_process.md
deleted file mode 100644
index 6f991cc4e4..0000000000
--- a/docs/federated/docs/source_zh_cn/image_classfication_dataset_process.md
+++ /dev/null
@@ -1,451 +0,0 @@
-# 联邦学习图像分类数据集处理
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/image_classfication_dataset_process.md)
-
-本教程采用`leaf`数据集中的联邦学习数据集`FEMNIST`，该数据集包含62个不同类别的手写数字和字母（数字0~9、26个小写字母、26个大写字母），图像大小为`28 x 28`像素，数据集包含3500个用户的手写数字和字母（最多可模拟3500个客户端参与联邦学习），总数据量为805263，平均每个用户包含数据量为226.83，所有用户数据量的方差为88.94。
-
-参考[leaf数据集官方指导](https://github.com/TalwalkarLab/leaf)下载数据集。
-
-1. 下载数据集前的环境要求。
-
-    ```sh
-    numpy==1.16.4
-    scipy                      # conda install scipy
-    tensorflow==1.13.1         # pip install tensorflow
-    Pillow                     # pip install Pillow
-    matplotlib                 # pip install matplotlib
-    jupyter                    # conda install jupyter notebook==5.7.8 tornado==4.5.3
-    pandas                     # pip install pandas
-    ```
-
-2. 使用git下载官方数据集生成脚本。
-
-    ```sh
-    git clone https://github.com/TalwalkarLab/leaf.git
-    ```
-
-    下载项目后，目录结构如下：
-
-    ```sh
-    leaf/data/femnist
-        ├── data  # 用来存放指令生成的数据集
-        ├── preprocess  # 存放数据预处理的相关代码
-        ├── preprocess.sh  # femnist数据集生成shell脚本
-        └── README.md  # 官方数据集下载指导文档
-    ```
-
-3. 以`femnist`数据集为例，运行以下指令进入指定路径。
-
-    ```sh
-    cd  leaf/data/femnist
-    ```
-
-4. 用指令`./preprocess.sh -s niid --sf 1.0 -k 0 -t sample`生成的数据集包含3500个用户，且按照9:1对每个用户的数据划分训练和测试集。
-
-    指令中参数含义可参考`leaf/data/femnist/README.md`文件中的说明。
-
-    运行之后目录结构如下：
-
-    ```text
-    leaf/data/femnist/35_client_sf1_data/
-        ├── all_data  # 所有数据集混合在一起，不区分训练测试集，共包含35个json文件，每个json文件包含100个用户的数据
-        ├── test  # 按照9:1对每个用户的数据划分训练和测试集后的测试集，共包含35个json文件，每个json文件包含100个用户的数据
-        ├── train  # 按照9:1对每个用户的数据划分训练和测试集后的训练集，共包含35个json文件，每个json文件包含100个用户的数据
-        └── ...  # 其他文件，暂不需要用到，不作介绍
-    ```
-
-    其中每个json文件包含以下三个部分：
-
-    - `users`: 用户列表。
-    - `num_samples`: 每个用户的样本数量列表。
-    - `user_data`: 一个以用户名为key，以它们各自的数据为value的字典对象；对于每个用户，数据表示为图像列表，每张图像表示为大小为784的整数列表（将`28 x 28`图像数组展平所得）。
-
-    在重新运行`preprocess.sh`之前，请确保删除数据目录中的`rem_user_data`、`sampled_data`、`test`和`train`子文件夹。
-
-5. 将35个json文件划分为3500个json文件（每个json文件代表一个用户）。
-
-    参考代码如下：
-
-    ```python
-    import os
-    import json
-
-    def mkdir(path):
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-    def partition_json(root_path, new_root_path):
-        """
-        partition 35 json files to 3500 json file
-
-        Each raw .json file is an object with 3 keys:
-        1. 'users', a list of users
-        2. 'num_samples', a list of the number of samples for each user
-        3. 'user_data', an object with user names as keys and their respective data as values; for each user, data is represented as a list of images, with each image represented as a size-784 integer list (flattened from 28 by 28)
-
-        Each new .json file is an object with 3 keys:
-        1. 'user_name', the name of user
-        2. 'num_samples', the number of samples for the user
-        3. 'user_data', an dict object with 'x' as keys and their respective data as values; with 'y' as keys and their respective label as values;
-
-        Args:
-            root_path (str): raw root path of 35 json files
-            new_root_path (str): new root path of 3500 json files
-        """
-        paths = os.listdir(root_path)
-        count = 0
-        file_num = 0
-        for i in paths:
-            file_num += 1
-            file_path = os.path.join(root_path, i)
-            print('======== process ' + str(file_num) + ' file: ' + str(file_path) + '======================')
-            with open(file_path, 'r') as load_f:
-                load_dict = json.load(load_f)
-                users = load_dict['users']
-                num_users = len(users)
-                num_samples = load_dict['num_samples']
-                for j in range(num_users):
-                    count += 1
-                    print('---processing user: ' + str(count) + '---')
-                    cur_out = {'user_name': None, 'num_samples': None, 'user_data': {}}
-                    cur_user_id = users[j]
-                    cur_data_num = num_samples[j]
-                    cur_user_path = os.path.join(new_root_path, cur_user_id + '.json')
-                    cur_out['user_name'] = cur_user_id
-                    cur_out['num_samples'] = cur_data_num
-                    cur_out['user_data'].update(load_dict['user_data'][cur_user_id])
-                    with open(cur_user_path, 'w') as f:
-                        json.dump(cur_out, f)
-        f = os.listdir(new_root_path)
-        print(len(f), ' users have been processed!')
-    # partition train json files
-    partition_json("leaf/data/femnist/35_client_sf1_data/train", "leaf/data/femnist/3500_client_json/train")
-    # partition test json files
-    partition_json("leaf/data/femnist/35_client_sf1_data/test", "leaf/data/femnist/3500_client_json/test")
-    ```
-
-    其中`root_path`为`leaf/data/femnist/35_client_sf1_data/{train,test}`，`new_root_path`自行设置，用于存放生成的3500个用户json文件，需分别对训练和测试文件夹进行处理。
-
-    新生成的3500个用户json文件，每个文件均包含以下三个部分：
-
-    - `user_name`: 用户名。
-    - `num_samples`: 用户的样本数。
-    - `user_data`: 一个以'x'为key，以用户数据为value的字典对象；以'y'为key，以用户数据对应的标签为value。
-
-    运行该脚本打印如下，代表运行成功：
-
-    ```sh
-    ======== process 1 file: /leaf/data/femnist/35_client_sf1_data/train/all_data_16_niid_0_keep_0_train_9.json======================
-    ---processing user: 1---
-    ---processing user: 2---
-    ---processing user: 3---
-    ......
-    ```
-
-6. 将json文件转换为图片文件。
-
-    可参考如下代码：
-
-    ```python
-    import os
-    import json
-    import numpy as np
-    from PIL import Image
-
-    name_list = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
-                 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U',
-                 'V', 'W', 'X', 'Y', 'Z',
-                 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',
-                 'v', 'w', 'x', 'y', 'z'
-                 ]
-
-    def mkdir(path):
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-    def json_2_numpy(img_size, file_path):
-        """
-        read json file to numpy
-        Args:
-            img_size (list): contain three elements: the height, width, channel of image
-            file_path (str): root path of 3500 json files
-        return:
-            image_numpy (numpy)
-            label_numpy (numpy)
-        """
-        # open json file
-        with open(file_path, 'r') as load_f_train:
-            load_dict = json.load(load_f_train)
-            num_samples = load_dict['num_samples']
-            x = load_dict['user_data']['x']
-            y = load_dict['user_data']['y']
-            size = (num_samples, img_size[0], img_size[1], img_size[2])
-            image_numpy = np.array(x, dtype=np.float32).reshape(size)  # mindspore doesn't support float64 and int64
-            label_numpy = np.array(y, dtype=np.int32)
-        return image_numpy, label_numpy
-
-    def json_2_img(json_path, save_path):
-        """
-        transform single json file to images
-
-        Args:
-            json_path (str): the path json file
-            save_path (str): the root path to save images
-
-        """
-        data, label = json_2_numpy([28, 28, 1], json_path)
-        for i in range(data.shape[0]):
-            img = data[i] * 255  # PIL don't support the 0/1 image ,need convert to 0~255 image
-            im = Image.fromarray(np.squeeze(img))
-            im = im.convert('L')
-            img_name = str(label[i]) + '_' + name_list[label[i]] + '_' + str(i) + '.png'
-            path1 = os.path.join(save_path, str(label[i]))
-            mkdir(path1)
-            img_path = os.path.join(path1, img_name)
-            im.save(img_path)
-            print('-----', i, '-----')
-
-    def all_json_2_img(root_path, save_root_path):
-        """
-        transform json files to images
-        Args:
-            json_path (str): the root path of 3500 json files
-            save_path (str): the root path to save images
-        """
-        usage = ['train', 'test']
-        for i in range(2):
-            x = usage[i]
-            files_path = os.path.join(root_path, x)
-            files = os.listdir(files_path)
-
-            for name in files:
-                user_name = name.split('.')[0]
-                json_path = os.path.join(files_path, name)
-                save_path1 = os.path.join(save_root_path, user_name)
-                mkdir(save_path1)
-                save_path = os.path.join(save_path1, x)
-                mkdir(save_path)
-                print('=============================' + name + '=======================')
-                json_2_img(json_path, save_path)
-
-    all_json_2_img("leaf/data/femnist/3500_client_json/", "leaf/data/femnist/3500_client_img/")
-    ```
-
-    运行该脚本打印如下，代表运行成功：
-
-    ```sh
-    =============================f0644_19.json=======================
-    ----- 0 -----
-    ----- 1 -----
-    ----- 2 -----
-    ......
-    ```
-
-7. 由于有些用户文件夹下的数据集较小，若数量小于batch size，需要进行随机扩充。
-
-    可参考下面代码对整个数据集`"leaf/data/femnist/3500_client_img/"`进行检查并扩充：
-
-    ```python
-    import os
-    import shutil
-    from random import choice
-
-    def count_dir(path):
-        num = 0
-        for root, dirs, files in os.walk(path):
-            for file in files:
-                num += 1
-        return num
-
-    def get_img_list(path):
-        img_path_list = []
-        label_list = os.listdir(path)
-        for i in range(len(label_list)):
-            label = label_list[i]
-            imgs_path = os.path.join(path, label)
-            imgs_name = os.listdir(imgs_path)
-            for j in range(len(imgs_name)):
-                img_name = imgs_name[j]
-                img_path = os.path.join(imgs_path, img_name)
-                img_path_list.append(img_path)
-        return img_path_list
-
-    def data_aug(data_root_path, batch_size = 32):
-        users = os.listdir(data_root_path)
-        tags = ["train", "test"]
-        aug_users = []
-        for i in range(len(users)):
-            user = users[i]
-            for tag in tags:
-                data_path = os.path.join(data_root_path, user, tag)
-                num_data = count_dir(data_path)
-                if num_data < batch_size:
-                    aug_users.append(user + "_" + tag)
-                    print("user: ", user, " ", tag, " data number: ", num_data, " < ", batch_size, " should be aug")
-                    aug_num = batch_size - num_data
-                    img_path_list = get_img_list(data_path)
-                    for j in range(aug_num):
-                        img_path = choice(img_path_list)
-                        info = img_path.split(".")
-                        aug_img_path = info[0] + "_aug_" + str(j) + ".png"
-                        shutil.copy(img_path, aug_img_path)
-                        print("[aug", j, "]", "============= copy file:", img_path, "to ->", aug_img_path)
-        print("the number of all aug users: " + str(len(aug_users)))
-        print("aug user name: ", end=" ")
-        for k in range(len(aug_users)):
-            print(aug_users[k], end = " ")
-
-    if __name__ == "__main__":
-        data_root_path = "leaf/data/femnist/3500_client_img/"
-        batch_size = 32
-        data_aug(data_root_path,  batch_size)
-    ```
-
-8. 将扩充后图片数据集转换为联邦学习框架可用的bin文件格式。
-
-    可参考下面代码：
-
-    ```python
-    import numpy as np
-    import os
-    import mindspore.dataset as ds
-    import mindspore.dataset.vision as vision
-    import mindspore.dataset.transforms as transforms
-    import mindspore
-
-    def mkdir(path):
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-    def count_id(path):
-        files = os.listdir(path)
-        ids = {}
-        for i in files:
-            ids[i] = int(i)
-        return ids
-
-    def create_dataset_from_folder(data_path, img_size, batch_size=32, repeat_size=1, num_parallel_workers=1, shuffle=False):
-        """ create dataset for train or test
-            Args:
-                data_path: Data path
-                batch_size: The number of data records in each group
-                repeat_size: The number of replicated data records
-                num_parallel_workers: The number of parallel workers
-            """
-        # define dataset
-        ids = count_id(data_path)
-        mnist_ds = ds.ImageFolderDataset(dataset_dir=data_path, decode=False, class_indexing=ids)
-        # define operation parameters
-        resize_height, resize_width = img_size[0], img_size[1]  # 32
-
-        transform = [
-            vision.Decode(True),
-            vision.Grayscale(1),
-            vision.Resize(size=(resize_height, resize_width)),
-            vision.Grayscale(3),
-            vision.ToTensor(),
-        ]
-        compose = transforms.Compose(transform)
-
-        # apply map operations on images
-        mnist_ds = mnist_ds.map(input_columns="label", operations=transforms.TypeCast(mindspore.int32))
-        mnist_ds = mnist_ds.map(input_columns="image", operations=compose)
-
-        # apply DatasetOps
-        buffer_size = 10000
-        if shuffle:
-            mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
-        mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
-        mnist_ds = mnist_ds.repeat(repeat_size)
-        return mnist_ds
-
-    def img2bin(root_path, root_save):
-        """
-        transform images to bin files
-
-        Args:
-        root_path: the root path of 3500 images files
-        root_save: the root path to save bin files
-
-        """
-
-        use_list = []
-        train_batch_num = []
-        test_batch_num = []
-        mkdir(root_save)
-        users = os.listdir(root_path)
-        for user in users:
-            use_list.append(user)
-            user_path = os.path.join(root_path, user)
-            train_test = os.listdir(user_path)
-            for tag in train_test:
-                data_path = os.path.join(user_path, tag)
-                dataset = create_dataset_from_folder(data_path, (32, 32, 1), 32)
-                batch_num = 0
-                img_list = []
-                label_list = []
-                for data in dataset.create_dict_iterator():
-                    batch_x_tensor = data['image']
-                    batch_y_tensor = data['label']
-                    trans_img = np.transpose(batch_x_tensor.asnumpy(), [0, 2, 3, 1])
-                    img_list.append(trans_img)
-                    label_list.append(batch_y_tensor.asnumpy())
-                    batch_num += 1
-
-                if tag == "train":
-                    train_batch_num.append(batch_num)
-                elif tag == "test":
-                    test_batch_num.append(batch_num)
-
-                imgs = np.array(img_list)  # (batch_num, 32,3,32,32)
-                labels = np.array(label_list)
-                path1 = os.path.join(root_save, user)
-                mkdir(path1)
-                image_path = os.path.join(path1, user + "_" + "bn_" + str(batch_num) + "_" + tag + "_data.bin")
-                label_path = os.path.join(path1, user + "_" + "bn_" + str(batch_num) + "_" + tag + "_label.bin")
-
-                imgs.tofile(image_path)
-                labels.tofile(label_path)
-                print("user: " + user + " " + tag + "_batch_num: " + str(batch_num))
-        print("total " + str(len(use_list)) + " users finished!")
-
-    root_path = "leaf/data/femnist/3500_client_img/"
-    root_save = "leaf/data/femnist/3500_clients_bin"
-    img2bin(root_path, root_save)
-    ```
-
-    运行该脚本打印如下，代表运行成功：
-
-    ```sh
-    user: f0141_43 test_batch_num: 1
-    user: f0141_43 train_batch_num: 10
-    user: f0137_14 test_batch_num: 1
-    user: f0137_14 train_batch_num: 11
-    ......
-    total 3500 users finished!
-    ```
-
-9. 生成`3500_clients_bin`文件夹内共包含3500个用户文件夹，其目录结构如下：
-
-    ```sh
-    leaf/data/femnist/3500_clients_bin
-      ├── f0000_14  # 用户编号
-      │   ├── f0000_14_bn_10_train_data.bin  # 用户f0000_14的训练数据 （bn_后面的数字10代表batch number）
-      │   ├── f0000_14_bn_10_train_label.bin  # 用户f0000_14的训练标签
-      │   ├── f0000_14_bn_1_test_data.bin  # 用户f0000_14的测试数据  （bn_后面的数字1代表batch number）
-      │   └── f0000_14_bn_1_test_label.bin  # 用户f0000_14的测试标签
-      ├── f0001_41  # 用户编号
-      │   ├── f0001_41_bn_11_train_data.bin  # 用户f0001_41的训练数据 （bn_后面的数字11代表batch number）
-      │   ├── f0001_41_bn_11_train_label.bin  # 用户f0001_41的训练标签
-      │   ├── f0001_41_bn_1_test_data.bin  # 用户f0001_41的测试数据 （bn_后面的数字1代表batch number）
-      │   └── f0001_41_bn_1_test_label.bin  # 用户f0001_41的测试标签
-      │                    ...
-      └── f4099_10  # 用户编号
-          ├── f4099_10_bn_4_train_data.bin  # 用户f4099_10的训练数据 （bn_后面的数字4代表batch number）
-          ├── f4099_10_bn_4_train_label.bin  # 用户f4099_10的训练标签
-          ├── f4099_10_bn_1_test_data.bin  # 用户f4099_10的测试数据 （bn_后面的数字1代表batch number）
-          └── f4099_10_bn_1_test_label.bin  # 用户f4099_10的测试标签
-    ```
-
-根据以上1～9步骤生成的`3500_clients_bin`文件夹可直接作为端云联邦图像分类任务的输入数据。
-
diff --git a/docs/federated/docs/source_zh_cn/image_classification_application.md b/docs/federated/docs/source_zh_cn/image_classification_application.md
deleted file mode 100644
index ff194ff3b2..0000000000
--- a/docs/federated/docs/source_zh_cn/image_classification_application.md
+++ /dev/null
@@ -1,331 +0,0 @@
-# 实现一个端云联邦的图像分类应用(x86)
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/image_classification_application.md)
-
-根据参与客户端的类型，联邦学习可分为云云联邦学习（cross-silo）和端云联邦学习（cross-device）。在云云联邦学习场景中，参与联邦学习的客户端是不同的组织（例如，医疗或金融）或地理分布的数据中心，即在多个数据孤岛上训练模型。在端云联邦学习场景中，参与的客户端为大量的移动或物联网设备。本框架将介绍如何在MindSpore端云联邦框架上使用网络LeNet实现一个图片分类应用，并提供在x86环境中模拟启动多客户端参与联邦学习的相关教程。
-
-在动手进行实践之前，确保你已经正确安装了MindSpore。如果没有，可以参考[MindSpore安装页面](https://www.mindspore.cn/install)完成安装。
-
-## 准备工作
-
-我们提供了可供用户直接使用的[联邦学习图像分类数据集FEMNIST](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/federated/3500_clients_bin.zip)，以及`.ms`格式的[端侧模型文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/lenet_train.ms)。用户也可以根据实际需求，参考以下教程自行生成数据集和模型。
-
-### 生成端侧模型文件
-
-1. 定义网络和训练过程。
-
-   具体网络和训练过程的定义可参考[快速入门](https://www.mindspore.cn/tutorials/zh-CN/master/beginner/quick_start.html#网络构建)。
-
-2. 将模型导出为MindIR格式文件。
-
-   代码片段如下：
-
-   ```python
-   import argparse
-   import numpy as np
-   import mindspore as ms
-   import mindspore.nn as nn
-
-   def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-       """weight initial for conv layer"""
-       weight = weight_variable()
-       return nn.Conv2d(
-           in_channels,
-           out_channels,
-           kernel_size=kernel_size,
-           stride=stride,
-           padding=padding,
-           weight_init=weight,
-           has_bias=False,
-           pad_mode="valid",
-       )
-
-   def fc_with_initialize(input_channels, out_channels):
-       """weight initial for fc layer"""
-       weight = weight_variable()
-       bias = weight_variable()
-       return nn.Dense(input_channels, out_channels, weight, bias)
-
-   def weight_variable():
-       """weight initial"""
-       return ms.common.initializer.TruncatedNormal(0.02)
-
-   class LeNet5(nn.Cell):
-       def __init__(self, num_class=10, channel=3):
-           super(LeNet5, self).__init__()
-           self.num_class = num_class
-           self.conv1 = conv(channel, 6, 5)
-           self.conv2 = conv(6, 16, 5)
-           self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-           self.fc2 = fc_with_initialize(120, 84)
-           self.fc3 = fc_with_initialize(84, self.num_class)
-           self.relu = nn.ReLU()
-           self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-           self.flatten = nn.Flatten()
-
-       def construct(self, x):
-           x = self.conv1(x)
-           x = self.relu(x)
-           x = self.max_pool2d(x)
-           x = self.conv2(x)
-           x = self.relu(x)
-           x = self.max_pool2d(x)
-           x = self.flatten(x)
-           x = self.fc1(x)
-           x = self.relu(x)
-           x = self.fc2(x)
-           x = self.relu(x)
-           x = self.fc3(x)
-           return x
-
-   parser = argparse.ArgumentParser(description="export mindir for lenet")
-   parser.add_argument("--device_target", type=str, default="CPU")
-   parser.add_argument("--mindir_path", type=str,
-                       default="lenet_train.mindir")  # the mindir file path of the model to be export
-
-   args, _ = parser.parse_known_args()
-   device_target = args.device_target
-   mindir_path = args.mindir_path
-
-   ms.set_context(mode=ms.GRAPH_MODE, device_target=device_target)
-
-   if __name__ == "__main__":
-       np.random.seed(0)
-       network = LeNet5(62)
-       criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=False, reduction="mean")
-       net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
-       net_with_criterion = nn.WithLossCell(network, criterion)
-       train_network = nn.TrainOneStepCell(net_with_criterion, net_opt)
-       train_network.set_train()
-
-       data = ms.Tensor(np.random.rand(32, 3, 32, 32).astype(np.float32))
-       label = ms.Tensor(np.random.randint(0, 1, (32, 62)).astype(np.float32))
-       ms.export(train_network, data, label, file_name=mindir_path,
-                 file_format='MINDIR')  # Add the export statement to obtain the model file in MindIR format.
-   ```
-
-   参数`--mindir_path`用于设置生成的MindIR格式文件路径。
-
-3. 将MindIR文件转化为联邦学习端侧框架可用的ms文件。
-
-   模型转换可参考[训练模型转换教程](https://www.mindspore.cn/lite/docs/zh-CN/master/converter/converter_tool.html)。
-
-   模型转换示例如下：
-
-   假设待转换的模型文件为`lenet_train.mindir`，执行如下转换命令：
-
-   ```sh
-   ./converter_lite --fmk=MINDIR --trainModel=true --modelFile=lenet_train.mindir --outputFile=lenet_train
-   ```
-
-   转换成功输出如下：
-
-   ```sh
-   CONVERT RESULT SUCCESS:0
-   ```
-
-   这表明MindSpore模型成功转换为MindSpore端侧模型，并生成了新文件`lenet_train.ms`。如果转换失败输出如下：
-
-   ```sh
-   CONVERT RESULT FAILED:
-   ```
-
-   生成的`.ms`格式的模型文件为后续客户端所需的模型文件。
-
-## 模拟启动多客户端参与联邦学习
-
-### 为客户端准备好模型文件
-
-本例在端侧使用lenet模拟实际用的网络，其中lenet的`.ms`格式的[端侧模型文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/lenet_train.ms)，由于真实场景一个客户端只包含一个.ms格式的模型文件，在模拟场景中，需要拷贝多份.ms文件，并按照`lenet_train{i}.ms`格式进行命名。其中i代表客户端编号，由于`run_client_x86.py`中，已自动为每个客户端拷贝.ms文件。
-具体见[启动脚本](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/simulate_x86/run_client_x86.py)中的copy_ms函数。
-
-### 启动云侧服务
-
-用户可先参考[横向云侧部署教程](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_server.html)部署云侧环境，并启动云侧服务。
-
-### 启动客户端
-
-启动客户端之前请先参照[横向端侧部署教程](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)进行端侧环境部署。
-
-使用提供的[run_client_x86.py](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/simulate_x86/run_client_x86.py)脚本进行端侧联邦学习的启动，通过相关参数的设置，来启动不同的联邦学习接口。
-待云侧服务启动成功之后，使用提供run_client_x86.py的脚本，调用联邦学习框架jar包`mindspore-lite-java-flclient.jar` 和模型脚本对应的jar包`quick_start_flclient.jar`（可参考[横向端侧部署中编译出包流程](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)获取）来模拟启动多客户端参与联邦学习任务。
-
-以LeNet网络为例，`run_client_x86.py`脚本中部分入参含义如下，用户可根据实际情况进行设置：
-
-- `--fl_jar_path`
-
-    设置联邦学习jar包路径，x86环境联邦学习jar包获取可参考[横向端侧部署中编译出包流程](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)。
-
-- `--case_jar_path`
-
-    设置模型脚本所生成的jar包`quick_start_flclient.jar`的路径，x86环境联邦学习jar包获取可参考[横向联邦端侧部署中编译出包流程](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)。
-
-- `--lite_jar_path`
-
-    设置mindspore lite的端侧jar包`mindspore-lite-java.jar`的路径，位于端侧包mindspore-lite-{version}-linux-x64.tar.gz中，x86环境联邦学习jar包获取可参考[横向端侧部署中构建环境依赖](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)。
-
-- `--train_data_dir`
-
-    训练数据集root路径，LeNet图片分类任务在该root路径中存放的是每个客户端的训练data.bin文件与label.bin文件，例如`data/femnist/3500_clients_bin/`。
-
-- `--fl_name`
-
-    联邦学习使用的模型脚本包路径。我们提供了两个类型的模型脚本供大家参考（[有监督情感分类任务](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert)、[LeNet图片分类任务](https://gitee.com/mindspore/federated/tree/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet)），对于有监督情感分类任务，该参数可设置为所提供的脚本文件[AlBertClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/albert/AlbertClient.java) 的包路径`com.mindspore.flclient.demo.albert.AlbertClient`；对于LeNet图片分类任务，该参数可设置为所提供的脚本文件[LenetClient.java](https://gitee.com/mindspore/federated/blob/master/example/quick_start_flclient/src/main/java/com/mindspore/flclient/demo/lenet/LenetClient.java) 的包路径`com.mindspore.flclient.demo.lenet.LenetClient`。同时，用户可参考这两个类型的模型脚本，自定义模型脚本，然后将该参数设置为自定义的模型文件ModelClient.java（需继承于类[Client.java](https://gitee.com/mindspore/federated/blob/master/mindspore_federated/device_client/src/main/java/com/mindspore/flclient/model/Client.java)）的包路径即可。
-
-- `--train_model_dir`
-
-    设置联邦学习使用的训练模型路径，为上面教程中拷贝的多份.ms文件所存放的目录，比如`ms/lenet`，必须为绝对路径。
-
-- `--domain_name`
-
-    用于设置端云通信url，目前，可支持https和http通信，对应格式分别为：https://......、http://......，当`if_use_elb`设置为true时，格式必须为： 或者 ，其中`127.0.0.1`对应提供云侧服务的机器ip（即云侧参数`--scheduler_ip`），`6666`对应云侧参数`--fl_server_port`。
-
-    注意1，当该参数设置为`http://......`时代表使用HTTP通信，可能会存在通信安全风险，请知悉。
-
-    注意2，当该参数设置为`https://......`代表使用HTTPS通信。此时必须进行SSL证书认证，需要通过参数`--cert_path`设置证书路径。
-
-- `--task`
-
-    用于设置本此启动的任务类型，为`train`代表启动训练任务，为`inference`代表启动多条数据推理任务，为`getModel`代表启动获取云侧模型的任务，设置其他字符串代表启动单条数据推理任务。默认为`train`。由于初始的模型文件(.ms文件)是未训练过的，建议先启动训练任务，待训练完成之后，再启动推理任务（注意两次启动的`client_num`保持一致，以保证`inference`使用的模型文件与`train`保持一致）。
-
-- `--batch_size`
-
-    设置联邦学习训练和推理时使用的单步训练样本数，即batch size。需与模型的输入数据的batch size保持一致。
-
-- `--client_num`
-
-    设置client数量，与启动server端时的`start_fl_job_cnt`保持一致，真实场景不需要此参数。
-
-若想进一步了解`run_client_x86.py`脚本中其他参数含义，可参考脚本中注释部分。
-
-联邦学习接口基本启动指令示例如下：
-
-```sh
- rm -rf client_*\
- && rm -rf ms/* \
- && python3 run_client_x86.py \
- --fl_jar_path="federated/mindspore_federated/device_client/build/libs/jarX86/mindspore-lite-java-flclient.jar" \
- --case_jar_path="federated/example/quick_start_flclient/target/case_jar/quick_start_flclient.jar" \
- --lite_jar_path="federated/mindspore_federated/device_client/third/mindspore-lite-2.0.0-linux-x64/runtime/lib/mindspore-lite-java.jar" \
- --train_data_dir="federated/tests/st/simulate_x86/data/3500_clients_bin/" \
- --eval_data_dir="null" \
- --infer_data_dir="null" \
- --vocab_path="null" \
- --ids_path="null" \
- --path_regex="," \
- --fl_name="com.mindspore.flclient.demo.lenet.LenetClient" \
- --origin_train_model_path="federated/tests/st/simulate_x86/ms_files/lenet/lenet_train.ms" \
- --origin_infer_model_path="null" \
- --train_model_dir="ms" \
- --infer_model_dir="ms" \
- --ssl_protocol="TLSv1.2" \
- --deploy_env="x86" \
- --domain_name="http://10.*.*.*:8010" \
- --cert_path="CARoot.pem" --use_elb="false" \
- --server_num=1 \
- --task="train" \
- --thread_num=1 \
- --cpu_bind_mode="NOT_BINDING_CORE" \
- --train_weight_name="null" \
- --infer_weight_name="null" \
- --name_regex="::" \
- --server_mode="FEDERATED_LEARNING" \
- --batch_size=32 \
- --input_shape="null" \
- --client_num=8
-```
-
-注意，启动指令中涉及路径的必须给出绝对路径。
-
-以上指令代表启动8个客户端参与联邦学习训练任务，若启动成功，会在当前文件夹生成8个客户端对应的日志文件，查看日志文件内容可了解每个客户端的运行情况：
-
-```text
-./
-├── client_0
-│   └── client.log  # 客户端0的日志文件
-│           ......
-└── client_7
-    └── client.log  # 客户端4的日志文件
-```
-
-针对不同的接口和场景，只需根据参数含义，修改特定参数值即可，比如：
-
-- 启动联邦学习训练任务SyncFLJob.flJobRun()
-
-    当`基本启动指令`中 `--task`设置为`train`时代表启动该任务。
-
-    可通过指令`grep -r "average loss:" client_0/client.log`查看`client_0`在训练过程中每个epoch的平均loss，会有类似如下打印：
-
-    ```sh
-    INFO:  ----------epoch:0,average loss:4.1258564 ----------
-    ......
-    ```
-
-    也可通过指令`grep -r "evaluate acc:" client_0/client.log`查看`client_0`在每个联邦学习迭代中聚合后模型的验证精度，会有类似如下打印：
-
-    ```sh
-    INFO:  [evaluate] evaluate acc: 0.125
-    ......
-    ```
-
-    在云侧，可以通过设置yaml配置文件的`cluster_client_num`参数与`eval_type`参数来指定进行无监督聚类指标统计的客户端group id数量与算法类型，在云侧生成的`metrics.json`统计文件可以查询到无监督指标信息：
-
-    ```text
-    "unsupervisedEval":0.640
-    "unsupervisedEval":0.675
-    "unsupervisedEval":0.677
-    "unsupervisedEval":0.706
-    ......
-    ```
-
-- 启动推理任务SyncFLJob.modelInference()
-
-    当`基本启动指令`中 `--task`设置为`inference`时代表启动该任务。
-
-    可通过指令`grep -r "the predicted labels:" client_0/client.log`查看`client_0`的推理结果：
-
-    ```sh
-    INFO:  [model inference] the predicted labels: [0, 0, 0, 1, 1, 1, 2, 2, 2]
-    ......
-    ```
-
-- 启动获取云侧最新模型任务SyncFLJob.getModel()
-
-    当`基本启动指令`中 `--task`设置为`getModel`时代表启动该任务。
-
-    在日志文件中若有如下内容代表获取云侧最新模型成功：
-
-    ```sh
-    INFO:  [getModel] get response from server ok!
-    ```
-
-### 关闭客户端进程
-
-可参考[finish.py](https://gitee.com/mindspore/federated/blob/master/example/cross_device_lenet_femnist/simulate_x86/finish.py)脚本，具体如下：
-
-关闭客户端指令如下：
-
-```sh
-python finish.py --kill_tag=mindspore-lite-java-flclient
-```
-
-其中参数`--kill_tag`用于搜索该关键字对客户端进程进行kill，只需要设置`--jarPath`中的特殊关键字即可。默认为`mindspore-lite-java-flclient`，即联邦学习jar包名。
-用户可通过指令`ps -ef |grep "mindspore-lite-java-flclient"`查看进程是否还存在。
-
-50个客户端参与联邦学习训练任务实验结果。
-
-目前`3500_clients_bin`文件夹中包含3500个客户端的数据，本脚本最多可模拟3500个客户端参与联邦学习。
-
-下图给出了50个客户端(设置`server_num`为16)进行联邦学习的测试集精度：
-
-![lenet_50_clients_acc](images/lenet_50_clients_acc.png)
-
-其中联邦学习总迭代数为100，客户端本地训练epoch数为20，batchSize设置为32。
-
-图中测试精度指对于每个联邦学习迭代，各客户端测试集在云侧聚合后的模型上的精度。
-
-AVG：对于每个联邦学习迭代，50个客户端测试集精度的平均值。
-
-TOP5：对于每个联邦学习迭代，测试集精度最高的5个客户端的精度平均值。
-
-LOW5：对于每个联邦学习迭代，测试集精度最低的5个客户端的精度平均值。
diff --git a/docs/federated/docs/source_zh_cn/image_classification_application_in_cross_silo.md b/docs/federated/docs/source_zh_cn/image_classification_application_in_cross_silo.md
deleted file mode 100644
index 7f245d2e50..0000000000
--- a/docs/federated/docs/source_zh_cn/image_classification_application_in_cross_silo.md
+++ /dev/null
@@ -1,313 +0,0 @@
-# 实现一个云云联邦的图像分类应用(x86)
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/image_classification_application_in_cross_silo.md)
-
-根据参与客户端的类型，联邦学习可分为云云联邦学习（cross-silo）和端云联邦学习（cross-device）。在云云联邦学习场景中，参与联邦学习的客户端是不同的组织（例如，医疗或金融）或地理分布的数据中心，即在多个数据孤岛上训练模型。在端云联邦学习场景中，参与的客户端为大量的移动或物联网设备。本框架将介绍如何在MindSpore Federated云云联邦框架上，使用网络LeNet实现一个图片分类应用。
-
-启动云云联邦的图像分类应用的完整脚本可参考[这里](https://gitee.com/mindspore/federated/tree/master/example/cross_silo_femnist)。
-
-## 下载数据集
-
-本示例采用[leaf数据集](https://github.com/TalwalkarLab/leaf)中的联邦学习数据集`FEMNIST`，该数据集包含62个不同类别的手写数字和字母（数字0~9、26个小写字母、26个大写字母），图像大小为`28 x 28`像素，数据集包含3500个用户的手写数字和字母（最多可模拟3500个客户端参与联邦学习），总数据量为805263，平均每个用户包含数据量为226.83，所有用户数据量的方差为88.94。
-
-可参考文档[端云联邦学习图像分类数据集处理](https://www.mindspore.cn/federated/docs/zh-CN/master/image_classfication_dataset_process.html)中步骤1~7获取图片形式的3500个用户数据集`3500_client_img`。
-
-由于原始3500个用户数据集中每个用户数据量比较少，在云云联邦任务中会收敛太快，无法明显体现云云联邦框架的收敛效果，下面提供一个参考脚本，将指定数量的用户数据集合并为一个用户，以增加参与云云联邦任务的单个用户数据量，更好地模拟云云联邦框架实验。
-
-```python
-import os
-import shutil
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.mkdir(path)
-
-
-def combine_users(root_data_path, new_data_path, raw_user_num, new_user_num):
-    mkdir(new_data_path)
-    user_list = os.listdir(root_data_path)
-    num_per_user = int(raw_user_num / new_user_num)
-    for i in range(new_user_num):
-        print(
-            "========================== combine the raw {}~{} users to the new user: dataset_{} ==========================".format(
-                i * num_per_user, i * num_per_user + num_per_user - 1, i))
-        new_user = "dataset_" + str(i)
-        new_user_path = os.path.join(new_data_path, new_user)
-        mkdir(new_user_path)
-        for j in range(num_per_user):
-            index = i * new_user_num + j
-            user = user_list[index]
-            user_path = os.path.join(root_data_path, user)
-            tags = os.listdir(user_path)
-            print("------------- process the raw user: {} -------------".format(user))
-            for t in tags:
-                tag_path = os.path.join(user_path, t)
-                label_list = os.listdir(tag_path)
-                new_tag_path = os.path.join(new_user_path, t)
-                mkdir(new_tag_path)
-                for label in label_list:
-                    label_path = os.path.join(tag_path, label)
-                    img_list = os.listdir(label_path)
-                    new_label_path = os.path.join(new_tag_path, label)
-                    mkdir(new_label_path)
-
-                    for img in img_list:
-                        img_path = os.path.join(label_path, img)
-                        new_img_name = user + "_" + img
-                        new_img_path = os.path.join(new_label_path, new_img_name)
-                        shutil.copy(img_path, new_img_path)
-
-if __name__ == "__main__":
-    root_data_path = "cross_silo_femnist/femnist/3500_clients_img"
-    new_data_path = "cross_silo_femnist/femnist/35_7_client_img"
-    raw_user_num = 35
-    new_user_num = 7
-    combine_users(root_data_path, new_data_path, raw_user_num, new_user_num)
-```
-
-其中`root_data_path`为原始3500个用户数据集路径，`new_data_path`为合并后数据集的路径，`raw_user_num`指定用于合并的用户数据集总数（需<=3500），`new_user_num`用于设置将原始数据集合并为多少个用户。如示例代码中将从`cross_silo_femnist/femnist/3500_clients_img`中选取前35个用户，合并为7个用户数据集后存放在路径`cross_silo_femnist/femnist/35_7_client_img`（合并后的7个用户，每个用户包含原始的5个用户数据集）。
-
-如下打印代表合并数据集成功：
-
-```sh
-========================== combine the raw 0~4 users to the new user: dataset_0 ==========================
-------------- process the raw user: f1798_42 -------------
-------------- process the raw user: f2149_81 -------------
-------------- process the raw user: f4046_46 -------------
-------------- process the raw user: f1093_13 -------------
-------------- process the raw user: f1124_24 -------------
-========================== combine the raw 5~9 users to the new user: dataset_1 ==========================
-------------- process the raw user: f0586_11 -------------
-------------- process the raw user: f0721_31 -------------
-------------- process the raw user: f3527_33 -------------
-------------- process the raw user: f0146_33 -------------
-------------- process the raw user: f1272_09 -------------
-========================== combine the raw 10~14 users to the new user: dataset_2 ==========================
-------------- process the raw user: f0245_40 -------------
-------------- process the raw user: f2363_77 -------------
-------------- process the raw user: f3596_19 -------------
-------------- process the raw user: f2418_82 -------------
-------------- process the raw user: f2288_58 -------------
-========================== combine the raw 15~19 users to the new user: dataset_3 ==========================
-------------- process the raw user: f2249_75 -------------
-------------- process the raw user: f3681_31 -------------
-------------- process the raw user: f3766_48 -------------
-------------- process the raw user: f0537_35 -------------
-------------- process the raw user: f0614_14 -------------
-========================== combine the raw 20~24 users to the new user: dataset_4 ==========================
-------------- process the raw user: f2302_58 -------------
-------------- process the raw user: f3472_19 -------------
-------------- process the raw user: f3327_11 -------------
-------------- process the raw user: f1892_07 -------------
-------------- process the raw user: f3184_11 -------------
-========================== combine the raw 25~29 users to the new user: dataset_5 ==========================
-------------- process the raw user: f1692_18 -------------
-------------- process the raw user: f1473_30 -------------
-------------- process the raw user: f0909_04 -------------
-------------- process the raw user: f1956_19 -------------
-------------- process the raw user: f1234_26 -------------
-========================== combine the raw 30~34 users to the new user: dataset_6 ==========================
-------------- process the raw user: f0031_02 -------------
-------------- process the raw user: f0300_24 -------------
-------------- process the raw user: f4064_46 -------------
-------------- process the raw user: f2439_77 -------------
-------------- process the raw user: f1717_16 -------------
-```
-
-文件夹 `cross_silo_femnist/femnist/35_7_client_img`目录结构如下：
-
-```text
-35_7_client_img  # 将FeMnist数据集中35个用户合并为7个客户端数据（各包含5个用户数据）
-├── dataset_0  # 客户端0的数据集
-│   ├── train   # 训练数据集
-│   │   ├── 0  # 存放类别0对应的图片数据
-│   │   ├── 1  # 存放类别1对应的图片数据
-│   │   │        ......
-│   │   └── 61  # 存放类别61对应的图片数据
-│   └── test  # 测试数据集，目录结构同train
-│              ......
-│
-└── dataset_6  # 客户端6的数据集
-    ├── train   # 训练数据集
-    │   ├── 0  # 存放类别0对应的图片数据
-    │   ├── 1  # 存放类别1对应的图片数据
-    │   │        ......
-    │   └── 61  # 存放类别61对应的图片数据
-    └── test  # 测试数据集，目录结构同train
-```
-
-## 定义网络
-
-我们选择相对简单的LeNet网络。LeNet网络不包括输入层的情况下，共有7层：2个卷积层、2个下采样层（池化层）、3个全连接层。每层都包含不同数量的训练参数，如下图所示：
-
-![LeNet5](images/LeNet_5.jpg)
-
-> 更多的LeNet网络的介绍不在此赘述，希望详细了解LeNet网络，可以查询。
-
-本任务使用的网络可参考脚本[test_cross_silo_femnist.py](https://gitee.com/mindspore/federated/blob/master/example/cross_silo_femnist/test_cross_silo_femnist.py)。
-
-若想具体了解MindSpore中网络定义流程可参考[初学入门](https://www.mindspore.cn/tutorials/zh-CN/master/beginner/quick_start.html#网络构建)。
-
-## 启动云云联邦任务
-
-### 安装MindSpore和MindSpore Federated
-
-包括源码和下载发布版两种方式，支持CPU、GPU、Ascend硬件平台，根据硬件平台选择安装即可。安装步骤可参考[MindSpore安装指南](https://www.mindspore.cn/install)，[MindSpore Federated安装指南](https://www.mindspore.cn/federated/docs/zh-CN/master/federated_install.html)。
-
-目前联邦学习框架只支持Linux环境中部署，cross-silo联邦学习框架需要MindSpore版本号>=1.5.0。
-
-### 启动任务
-
-参考[示例](https://gitee.com/mindspore/federated/tree/master/example/cross_silo_femnist)，启动集群。参考示例目录结构如下：
-
-```text
-cross_silo_femnist/
-├── config.json # 配置文件
-├── finish_cross_silo_femnist.py # 关闭云云联邦任务脚本
-├── run_cross_silo_femnist_sched.py # 启动云云联邦scheduler脚本
-├── run_cross_silo_femnist_server.py # 启动云云联邦server脚本
-├── run_cross_silo_femnist_worker.py # 启动云云联邦worker脚本
-├── run_cross_silo_femnist_worker_distributed.py # 启动云云联邦分布式训练worker脚本
-└── test_cross_silo_femnist.py # 客户端使用的训练脚本
-```
-
-1. 启动Scheduler
-
-   `run_cross_silo_femnist_sched.py`是为用户启动`Scheduler`而提供的Python脚本，并支持通过`argparse`传参修改配置。执行指令如下，代表启动本次联邦学习任务的`Scheduler`，其TCP端口为`5554`。
-
-   ```sh
-   python run_cross_silo_femnist_sched.py --scheduler_manage_address=127.0.0.1:5554
-   ```
-
-   打印如下代表启动成功：
-
-   ```sh
-   [INFO] FEDERATED(35566,7f4275895740,python):2022-10-09-15:23:22.450.205 [mindspore_federated/fl_arch/ccsrc/scheduler/scheduler.cc:35] Run] Scheduler started successfully.
-   [INFO] FEDERATED(35566,7f41f259d700,python):2022-10-09-15:23:22.450.357 [mindspore_federated/fl_arch/ccsrc/common/communicator/http_request_handler.cc:90] Run] Start http server!
-   ```
-
-2. 启动Server
-
-   `run_cross_silo_femnist_server.py`是为用户启动若干`Server`而提供的Python脚本，并支持通过`argparse`传参修改配置。执行指令如下，代表启动本次联邦学习任务的`Server`，其http起始端口为`5555`，`server`数量为`4`个。
-
-   ```sh
-    python run_cross_silo_femnist_server.py --local_server_num=4 --http_server_address=10.*.*.*:5555
-   ```
-
-   以上指令等价于启动了4个`Server`进程，每个`Server`的联邦学习服务端口分别为`5555`、`5556`、`5557`和`5558`。
-
-3. 启动Worker
-
-   `run_cross_silo_femnist_worker.py`是为用户启动若干`worker`而提供的Python脚本，并支持通过`argparse`传参修改配置。执行指令如下，代表启动本次联邦学习任务的`worker`，其http起始端口为`5555`，`worker`数量为`4`个：
-
-   ```sh
-   python run_cross_silo_femnist_worker.py --dataset_path=/data_nfs/code/fed_user_doc/federated/tests/st/cross_silo_femnist/35_7_client_img/ --http_server_address=10.*.*.*:5555
-   ```
-
-   当前云云联邦的`worker`节点支持单机多卡&多机多卡的分布式训练方式，`run_cross_silo_femnist_worker_distributed.py`是为用户启动`worker`节点的分布式训练而提供的Python脚本，并支持通过`argparse`传参修改配置。执行指令如下，代表启动本次联邦学习任务的分布式`worker`，其中`device_num`表示`worker`集群启动的进程数目，`run_distribute`表示启动集群的分布式训练，其http起始端口为`5555`，`worker`进程数量为`4`个：
-
-   ```sh
-   python run_cross_silo_femnist_worker_distributed.py --device_num=4 --run_distribute=True --dataset_path=/data_nfs/code/fed_user_doc/federated/tests/st/cross_silo_femnist/35_7_client_img/ --http_server_address=10.*.*.*:5555
-   ```
-
-当执行以上三个指令之后，进入当前目录下`worker_0`文件夹，通过指令`grep -rn "test acc" *`查看`worker_0`日志，可看到如下类似打印：
-
-```sh
-local epoch: 0, loss: 3.787421340711655, trian acc: 0.05342741935483871, test acc: 0.075
-```
-
-则说明云云联邦启动成功，`worker_0`正在训练，其他worker可通过类似方式查看。
-
-若worker已分布式多卡训练的方式启动，进入当前目录下`worker_distributed/log_output/`文件夹，通过指令`grep -rn "test acc" *`查看`worker`分布式集群的日志，可看到如下类似打印：
-
-```text
-local epoch: 0, loss: 2.3467453340711655, trian acc: 0.06532451988877687, test acc: 0.076
-```
-
-以上脚本中参数配置说明请参考[yaml配置说明](https://www.mindspore.cn/federated/docs/zh-CN/master/horizontal/federated_server_yaml.html)。
-
-### 日志查看
-
-成功启动任务之后，会在当前目录`cross_silo_femnist`下生成相应日志文件，日志文件目录结构如下：
-
-```text
-cross_silo_femnist
-├── scheduler
-│   └── scheduler.log     # 运行scheduler过程中打印日志
-├── server_0
-│   └── server.log        # server_0运行过程中打印日志
-├── server_1
-│   └── server.log        # server_1运行过程中打印日志
-├── server_2
-│   └── server.log        # server_2运行过程中打印日志
-├── server_3
-│   └── server.log        # server_3运行过程中打印日志
-├── worker_0
-│   ├── ckpt              # 存放worker_0在每个联邦学习迭代结束时获取的聚合后的模型ckpt
-│   │   ├── 0-fl-ms-bs32-0epoch.ckpt
-│   │   ├── 0-fl-ms-bs32-1epoch.ckpt
-│   │   │
-│   │   │              ......
-│   │   │
-│   │   └── 0-fl-ms-bs32-19epoch.ckpt
-│   └── worker.log        # 记录worker_0参与联邦学习任务过程中输出日志
-└── worker_1
-    ├── ckpt              # 存放worker_1在每个联邦学习迭代结束时获取的聚合后的模型ckpt
-    │  ├── 1-fl-ms-bs32-0epoch.ckpt
-    │  ├── 1-fl-ms-bs32-1epoch.ckpt
-    │  │
-    │  │                     ......
-    │  │
-    │  └── 1-fl-ms-bs32-19epoch.ckpt
-    └── worker.log        # 记录worker_1参与联邦学习任务过程中输出日志
-```
-
-### 关闭任务
-
-若想中途退出，则可用以下指令：
-
-```sh
-python finish_cross_silo_femnist.py --redis_port=2345
-```
-
-或者等待训练任务结束之后集群会自动退出，不需要手动关闭。
-
-### 实验结果
-
-- 使用数据：
-
-  上面`下载数据集`部分生成的`35_7_client_img/`数据集
-
-- 客户端本地训练epoch数：20
-
-- 云云联邦学习总迭代数：20
-
-- 实验结果（每个迭代聚合后模型在客户端的测试集上精度）
-
-`worker_0`测试结果：
-
-```sh
-worker_0/worker.log:7409:local epoch: 0, loss: 3.787421340711655, trian acc: 0.05342741935483871, test acc: 0.075
-worker_0/worker.log:14419:local epoch: 1, loss: 3.725699281115686, trian acc: 0.05342741935483871, test acc: 0.075
-worker_0/worker.log:21429:local epoch: 2, loss: 3.5285709657335795, trian acc: 0.19556451612903225, test acc: 0.16875
-worker_0/worker.log:28439:local epoch: 3, loss: 3.0393165519160608, trian acc: 0.4889112903225806, test acc: 0.4875
-worker_0/worker.log:35449:local epoch: 4, loss: 2.575952764115026, trian acc: 0.6854838709677419, test acc: 0.60625
-worker_0/worker.log:42459:local epoch: 5, loss: 2.2081101375296512, trian acc: 0.7782258064516129, test acc: 0.6875
-worker_0/worker.log:49470:local epoch: 6, loss: 1.9229739431736557, trian acc: 0.8054435483870968, test acc: 0.69375
-worker_0/worker.log:56480:local epoch: 7, loss: 1.7005576549999293, trian acc: 0.8296370967741935, test acc: 0.65625
-worker_0/worker.log:63490:local epoch: 8, loss: 1.5248727620766704, trian acc: 0.8407258064516129, test acc: 0.6375
-worker_0/worker.log:70500:local epoch: 9, loss: 1.3838803705352127, trian acc: 0.8568548387096774, test acc: 0.7
-worker_0/worker.log:77510:local epoch: 10, loss: 1.265225578921041, trian acc: 0.8679435483870968, test acc: 0.7125
-worker_0/worker.log:84520:local epoch: 11, loss: 1.167484122101638, trian acc: 0.8659274193548387, test acc: 0.70625
-worker_0/worker.log:91530:local epoch: 12, loss: 1.082880981700859, trian acc: 0.8770161290322581, test acc: 0.65625
-worker_0/worker.log:98540:local epoch: 13, loss: 1.0097520119572772, trian acc: 0.8840725806451613, test acc: 0.64375
-worker_0/worker.log:105550:local epoch: 14, loss: 0.9469810053708015, trian acc: 0.9022177419354839, test acc: 0.7
-worker_0/worker.log:112560:local epoch: 15, loss: 0.8907848935604703, trian acc: 0.9022177419354839, test acc: 0.6625
-worker_0/worker.log:119570:local epoch: 16, loss: 0.8416629644123349, trian acc: 0.9082661290322581, test acc: 0.70625
-worker_0/worker.log:126580:local epoch: 17, loss: 0.798475691030866, trian acc: 0.9122983870967742, test acc: 0.70625
-worker_0/worker.log:133591:local epoch: 18, loss: 0.7599438544427897, trian acc: 0.9243951612903226, test acc: 0.6875
-worker_0/worker.log:140599:local epoch: 19, loss: 0.7250227383907605, trian acc: 0.9294354838709677, test acc: 0.7125
-```
-
-其他客户端的测试结果基本相同，不再一一列出。
\ No newline at end of file
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diff --git a/docs/federated/docs/source_zh_cn/images/cross-silo_fastrcnn-2workers-loss.png b/docs/federated/docs/source_zh_cn/images/cross-silo_fastrcnn-2workers-loss.png
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