From 9ac71505bead8f61fb88fcec4be5621416fd49d8 Mon Sep 17 00:00:00 2001 From: lvmingfu Date: Mon, 23 May 2022 16:06:13 +0800 Subject: [PATCH] fix error link from master to r1.7 --- docs/federated/docs/source_en/deploy_federated_client.md | 2 +- docs/federated/docs/source_en/deploy_federated_server.md | 2 +- docs/federated/docs/source_en/faq.md | 2 +- docs/federated/docs/source_en/federated_install.md | 2 +- .../docs/source_en/image_classification_application.md | 2 +- .../docs/source_en/interface_description_federated_client.md | 2 +- docs/federated/docs/source_en/java_api_callback.md | 2 +- docs/federated/docs/source_en/java_api_client.md | 2 +- docs/federated/docs/source_en/java_api_clientmanager.md | 2 +- docs/federated/docs/source_en/java_api_dataset.md | 2 +- docs/federated/docs/source_en/java_api_flparameter.md | 2 +- docs/federated/docs/source_en/java_api_syncfljob.md | 2 +- .../docs/source_en/local_differential_privacy_training_noise.md | 2 +- docs/federated/docs/source_en/pairwise_encryption_training.md | 2 +- .../docs/source_en/sentiment_classification_application.md | 2 +- docs/federated/docs/source_zh_cn/deploy_federated_client.md | 2 +- docs/federated/docs/source_zh_cn/deploy_federated_server.md | 2 +- docs/federated/docs/source_zh_cn/faq.md | 2 +- docs/federated/docs/source_zh_cn/federated_install.md | 2 +- .../docs/source_zh_cn/image_classification_application.md | 2 +- .../image_classification_application_in_cross_silo.md | 2 +- .../docs/source_zh_cn/interface_description_federated_client.md | 2 +- docs/federated/docs/source_zh_cn/java_api_callback.md | 2 +- docs/federated/docs/source_zh_cn/java_api_client.md | 2 +- docs/federated/docs/source_zh_cn/java_api_clientmanager.md | 2 +- docs/federated/docs/source_zh_cn/java_api_dataset.md | 2 +- docs/federated/docs/source_zh_cn/java_api_flparameter.md | 2 +- docs/federated/docs/source_zh_cn/java_api_syncfljob.md | 2 +- .../source_zh_cn/local_differential_privacy_training_noise.md | 2 +- .../source_zh_cn/local_differential_privacy_training_signds.md | 2 +- .../source_zh_cn/object_detection_application_in_cross_silo.md | 2 +- .../federated/docs/source_zh_cn/pairwise_encryption_training.md | 2 +- .../docs/source_zh_cn/sentiment_classification_application.md | 2 +- 33 files changed, 33 insertions(+), 33 deletions(-) diff --git a/docs/federated/docs/source_en/deploy_federated_client.md b/docs/federated/docs/source_en/deploy_federated_client.md index 0c50423e98..bbc4075415 100644 --- a/docs/federated/docs/source_en/deploy_federated_client.md +++ b/docs/federated/docs/source_en/deploy_federated_client.md @@ -1,6 +1,6 @@ # On-Device Deployment - + The following describes how to deploy the Federated-Client in the Android aarch and Linux x86_64 environments: diff --git a/docs/federated/docs/source_en/deploy_federated_server.md b/docs/federated/docs/source_en/deploy_federated_server.md index 3c586fbc12..46eae9c888 100644 --- a/docs/federated/docs/source_en/deploy_federated_server.md +++ b/docs/federated/docs/source_en/deploy_federated_server.md @@ -1,6 +1,6 @@ # Cloud-based Deployment - + The following uses LeNet as an example to describe how to use MindSpore to deploy a federated learning cluster. diff --git a/docs/federated/docs/source_en/faq.md b/docs/federated/docs/source_en/faq.md index ffa89d930a..c0885e8aa4 100644 --- a/docs/federated/docs/source_en/faq.md +++ b/docs/federated/docs/source_en/faq.md @@ -1,6 +1,6 @@ # FAQ - + **Q: If the cluster networking is unsuccessful, how to locate the cause?** diff --git a/docs/federated/docs/source_en/federated_install.md b/docs/federated/docs/source_en/federated_install.md index 4a8194813a..4e0d1caaba 100644 --- a/docs/federated/docs/source_en/federated_install.md +++ b/docs/federated/docs/source_en/federated_install.md @@ -1,6 +1,6 @@ # Obtaining MindSpore Federated - + ## Installation Overview diff --git a/docs/federated/docs/source_en/image_classification_application.md b/docs/federated/docs/source_en/image_classification_application.md index 1845604304..ddc87ac445 100644 --- a/docs/federated/docs/source_en/image_classification_application.md +++ b/docs/federated/docs/source_en/image_classification_application.md @@ -1,6 +1,6 @@ # Implementing an Image Classification Application of Cross-device Federated Learning (x86) - + Federated learning can be divided into cross-silo federated learning and cross-device federated learning according to different participating customers. In the cross-silo federation learning scenario, the customers participating in federated learning are different organizations (for example, medical or financial) or geographically distributed data centers, that is, training models on multiple data islands. The clients participating in the cross-device federation 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 federation framework, and provides related tutorials for simulating to start multi-client participation in federated learning in the x86 environment. diff --git a/docs/federated/docs/source_en/interface_description_federated_client.md b/docs/federated/docs/source_en/interface_description_federated_client.md index 5d5e733a0f..c47b71a695 100644 --- a/docs/federated/docs/source_en/interface_description_federated_client.md +++ b/docs/federated/docs/source_en/interface_description_federated_client.md @@ -1,6 +1,6 @@ # Examples - + 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. diff --git a/docs/federated/docs/source_en/java_api_callback.md b/docs/federated/docs/source_en/java_api_callback.md index 20e4403d69..3f796b7b68 100644 --- a/docs/federated/docs/source_en/java_api_callback.md +++ b/docs/federated/docs/source_en/java_api_callback.md @@ -1,6 +1,6 @@ # Callback - + ```java import com.mindspore.flclient.model.Callback diff --git a/docs/federated/docs/source_en/java_api_client.md b/docs/federated/docs/source_en/java_api_client.md index 41192a42b0..19893a5858 100644 --- a/docs/federated/docs/source_en/java_api_client.md +++ b/docs/federated/docs/source_en/java_api_client.md @@ -1,6 +1,6 @@ # Client - + ```java import com.mindspore.flclient.model.Client diff --git a/docs/federated/docs/source_en/java_api_clientmanager.md b/docs/federated/docs/source_en/java_api_clientmanager.md index 219a4b5ba4..f21fd3b23b 100644 --- a/docs/federated/docs/source_en/java_api_clientmanager.md +++ b/docs/federated/docs/source_en/java_api_clientmanager.md @@ -1,6 +1,6 @@ # ClientManager - + ```java import com.mindspore.flclient.model.ClientManager diff --git a/docs/federated/docs/source_en/java_api_dataset.md b/docs/federated/docs/source_en/java_api_dataset.md index 34744c396e..e1a7c9f2f6 100644 --- a/docs/federated/docs/source_en/java_api_dataset.md +++ b/docs/federated/docs/source_en/java_api_dataset.md @@ -1,6 +1,6 @@ # DataSet - + ```java import com.mindspore.flclient.model.DataSet diff --git a/docs/federated/docs/source_en/java_api_flparameter.md b/docs/federated/docs/source_en/java_api_flparameter.md index 459342b3e5..b2dee54812 100644 --- a/docs/federated/docs/source_en/java_api_flparameter.md +++ b/docs/federated/docs/source_en/java_api_flparameter.md @@ -1,6 +1,6 @@ # FLParameter - + ```java import com.mindspore.flclient.FLParameter diff --git a/docs/federated/docs/source_en/java_api_syncfljob.md b/docs/federated/docs/source_en/java_api_syncfljob.md index 83118199d3..8cf9950989 100644 --- a/docs/federated/docs/source_en/java_api_syncfljob.md +++ b/docs/federated/docs/source_en/java_api_syncfljob.md @@ -1,6 +1,6 @@ # SyncFLJob - + ```java import com.mindspore.flclient.SyncFLJob 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 index 0aaee69313..25d805cede 100644 --- a/docs/federated/docs/source_en/local_differential_privacy_training_noise.md +++ b/docs/federated/docs/source_en/local_differential_privacy_training_noise.md @@ -1,6 +1,6 @@ # Local differential privacy perturbation training - + 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. diff --git a/docs/federated/docs/source_en/pairwise_encryption_training.md b/docs/federated/docs/source_en/pairwise_encryption_training.md index 41110778b4..92d857ae20 100644 --- a/docs/federated/docs/source_en/pairwise_encryption_training.md +++ b/docs/federated/docs/source_en/pairwise_encryption_training.md @@ -1,6 +1,6 @@ # Pairwise encryption training - + 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. diff --git a/docs/federated/docs/source_en/sentiment_classification_application.md b/docs/federated/docs/source_en/sentiment_classification_application.md index ee356fa7b8..cd0c3f1fa8 100644 --- a/docs/federated/docs/source_en/sentiment_classification_application.md +++ b/docs/federated/docs/source_en/sentiment_classification_application.md @@ -1,6 +1,6 @@ # Implementing a Sentiment Classification Application (Android) - + In privacy compliance scenarios, the federated learning modeling mode based on device-cloud synergy can make full use of the advantages of device data and prevent sensitive user data from being directly reported to the cloud. When exploring the application scenarios of federated learning, we notice the input method scenario. Users attach great importance to their text privacy and intelligent functions on the input method. Therefore, federated learning is naturally applicable to the input method scenario. MindSpore Federated applies the federated language model to the emoji prediction function of the input method. The federated language model recommends emojis suitable for the current context based on the chat text data. During federated learning modeling, each emoji is defined as a sentiment label category, and each chat phrase corresponds to an emoji. MindSpore Federated defines the emoji prediction task as a federated sentiment classification task. diff --git a/docs/federated/docs/source_zh_cn/deploy_federated_client.md b/docs/federated/docs/source_zh_cn/deploy_federated_client.md index f3ca5134af..c35cea453d 100644 --- a/docs/federated/docs/source_zh_cn/deploy_federated_client.md +++ b/docs/federated/docs/source_zh_cn/deploy_federated_client.md @@ -1,6 +1,6 @@ # 端侧部署 - + 本文档分别介绍如何面向Android aarch环境和Linux x86_64环境，部署Federated-Client。 diff --git a/docs/federated/docs/source_zh_cn/deploy_federated_server.md b/docs/federated/docs/source_zh_cn/deploy_federated_server.md index 67bfb1dfcb..7b9dc8202e 100644 --- a/docs/federated/docs/source_zh_cn/deploy_federated_server.md +++ b/docs/federated/docs/source_zh_cn/deploy_federated_server.md @@ -1,6 +1,6 @@ # 云侧部署 - + 本文档以LeNet网络为例，讲解如何使用MindSpore部署联邦学习集群。 diff --git a/docs/federated/docs/source_zh_cn/faq.md b/docs/federated/docs/source_zh_cn/faq.md index 2c6df46a6c..d392d4bcaf 100644 --- a/docs/federated/docs/source_zh_cn/faq.md +++ b/docs/federated/docs/source_zh_cn/faq.md @@ -1,6 +1,6 @@ # FAQ - + **Q: 请问如果集群组网不成功，怎么定位原因？** diff --git a/docs/federated/docs/source_zh_cn/federated_install.md b/docs/federated/docs/source_zh_cn/federated_install.md index b18bf37df0..a69e8e6660 100644 --- a/docs/federated/docs/source_zh_cn/federated_install.md +++ b/docs/federated/docs/source_zh_cn/federated_install.md @@ -1,6 +1,6 @@ # 获取MindSpore Federated - + MindSpore Federated框架代码集成在云侧MindSpore和端侧MindSpore Lite框架中，因此需要分别获取MindSpore whl包和MindSpore Lite java安装包。其中，MindSpore Whl包负责云侧集群聚合训练，以及与Lite的通信。MindSpore Lite java安装包中包括两部分，一部分是MindSpore Lite训练安装包，负责模型的端侧本地训练，另一部分是Federated-Client安装包，负责模型的下发、加密以及与云侧MindSpore服务的交互。 diff --git a/docs/federated/docs/source_zh_cn/image_classification_application.md b/docs/federated/docs/source_zh_cn/image_classification_application.md index c7079a0532..58e5bfeb63 100644 --- a/docs/federated/docs/source_zh_cn/image_classification_application.md +++ b/docs/federated/docs/source_zh_cn/image_classification_application.md @@ -1,6 +1,6 @@ # 实现一个端云联邦的图像分类应用(x86) - + 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。 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 index cc503b59b7..00a5934470 100644 --- 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 @@ -1,6 +1,6 @@ # 实现一个云云联邦的图像分类应用(x86) - + 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云云联邦框架上，使用网络LeNet实现一个图片分类应用。 diff --git a/docs/federated/docs/source_zh_cn/interface_description_federated_client.md b/docs/federated/docs/source_zh_cn/interface_description_federated_client.md index 3469e66544..a7d1c72fc3 100644 --- a/docs/federated/docs/source_zh_cn/interface_description_federated_client.md +++ b/docs/federated/docs/source_zh_cn/interface_description_federated_client.md @@ -1,6 +1,6 @@ # 使用示例 - + 注意，在使用以下接口前，可先参照文档[端侧部署](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_client.html)进行相关环境的部署。 diff --git a/docs/federated/docs/source_zh_cn/java_api_callback.md b/docs/federated/docs/source_zh_cn/java_api_callback.md index 3814435ab0..9c1baa3b79 100644 --- a/docs/federated/docs/source_zh_cn/java_api_callback.md +++ b/docs/federated/docs/source_zh_cn/java_api_callback.md @@ -1,6 +1,6 @@ # Callback - + ```java import com.mindspore.flclient.model.Callback diff --git a/docs/federated/docs/source_zh_cn/java_api_client.md b/docs/federated/docs/source_zh_cn/java_api_client.md index c8c03634c4..9b02f82f90 100644 --- a/docs/federated/docs/source_zh_cn/java_api_client.md +++ b/docs/federated/docs/source_zh_cn/java_api_client.md @@ -1,6 +1,6 @@ # Client - + ```java import com.mindspore.flclient.model.Client diff --git a/docs/federated/docs/source_zh_cn/java_api_clientmanager.md b/docs/federated/docs/source_zh_cn/java_api_clientmanager.md index 7f2b950265..f66d9a8db4 100644 --- a/docs/federated/docs/source_zh_cn/java_api_clientmanager.md +++ b/docs/federated/docs/source_zh_cn/java_api_clientmanager.md @@ -1,6 +1,6 @@ # ClientManager - + ```java import com.mindspore.flclient.model.ClientManager diff --git a/docs/federated/docs/source_zh_cn/java_api_dataset.md b/docs/federated/docs/source_zh_cn/java_api_dataset.md index 5674318eec..f0d511002c 100644 --- a/docs/federated/docs/source_zh_cn/java_api_dataset.md +++ b/docs/federated/docs/source_zh_cn/java_api_dataset.md @@ -1,6 +1,6 @@ # DataSet - + ```java import com.mindspore.flclient.model.DataSet diff --git a/docs/federated/docs/source_zh_cn/java_api_flparameter.md b/docs/federated/docs/source_zh_cn/java_api_flparameter.md index d5814ee041..a95cfc671a 100644 --- a/docs/federated/docs/source_zh_cn/java_api_flparameter.md +++ b/docs/federated/docs/source_zh_cn/java_api_flparameter.md @@ -1,6 +1,6 @@ # FLParameter - + ```java import com.mindspore.flclient.FLParameter diff --git a/docs/federated/docs/source_zh_cn/java_api_syncfljob.md b/docs/federated/docs/source_zh_cn/java_api_syncfljob.md index e96943d0f0..54d55d9c57 100644 --- a/docs/federated/docs/source_zh_cn/java_api_syncfljob.md +++ b/docs/federated/docs/source_zh_cn/java_api_syncfljob.md @@ -1,6 +1,6 @@ # SyncFLJob - + ```java import com.mindspore.flclient.SyncFLJob diff --git a/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md index dede1fffc5..becdd3b0be 100644 --- a/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md +++ b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md @@ -1,6 +1,6 @@ # 局部差分隐私加噪训练 - + 联邦学习过程中，用户数据仅用于客户端设备的本地训练，不需要上传至中心服务器，可以避免泄露用户个人数据。然而，传统联邦学习框架中，模型以明文形式上云，仍然存在间接泄露用户隐私的风险。攻击者获取到客户端上传的明文模型后，可以通过重构、模型逆向等攻击方式，恢复参与学习的用户个人数据，导致用户隐私泄露。 diff --git a/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md index ede4e7701e..7d396d5403 100644 --- a/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md +++ b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md @@ -1,6 +1,6 @@ # 局部差分隐私SignDS训练 - + ## 隐私保护背景 diff --git a/docs/federated/docs/source_zh_cn/object_detection_application_in_cross_silo.md b/docs/federated/docs/source_zh_cn/object_detection_application_in_cross_silo.md index d4fba835d2..168b8120fd 100644 --- a/docs/federated/docs/source_zh_cn/object_detection_application_in_cross_silo.md +++ b/docs/federated/docs/source_zh_cn/object_detection_application_in_cross_silo.md @@ -1,6 +1,6 @@ # 实现一个云云联邦的目标检测应用(x86) - + 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云云联邦框架上使用网络Fast R-CNN实现一个目标检测应用。 diff --git a/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md b/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md index c3dfc794ec..3eb4c03247 100644 --- a/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md +++ b/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md @@ -1,6 +1,6 @@ # 安全聚合训练 - + 联邦学习过程中，用户数据仅用于本地设备训练，不需要上传至中心服务器，可以避免用户个人数据的直接泄露。然而传统联邦学习框架中，模型以明文形式上云，仍然存在间接泄露用户隐私的风险。攻击者获取到用户上传的明文模型后，可以通过重构、模型逆向等攻击方式恢复用户的个人训练数据，导致用户隐私泄露。 diff --git a/docs/federated/docs/source_zh_cn/sentiment_classification_application.md b/docs/federated/docs/source_zh_cn/sentiment_classification_application.md index 83f3ad9310..e93484291d 100644 --- a/docs/federated/docs/source_zh_cn/sentiment_classification_application.md +++ b/docs/federated/docs/source_zh_cn/sentiment_classification_application.md @@ -1,6 +1,6 @@ # 实现一个情感分类应用(Android) - + 通过端云协同的联邦学习建模方式，可以充分发挥端侧数据的优势，避免用户敏感数据直接上传云侧。由于用户在使用输入法时，十分重视所输入文字的隐私，且输入法的智慧功能对提升用户体验非常需要。因此，联邦学习天然适用于输入法应用场景。 -- Gitee