diff --git a/docs/federated/docs/source_en/deploy_federated_client.md b/docs/federated/docs/source_en/deploy_federated_client.md index 0c50423e987586946ccb1e5218f22f8f09396268..bbc4075415ab858a55bff4d85af904b653a8b8ae 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 3c586fbc12cb76f4b743efe6955751b83a64bad7..46eae9c8886cf885546618e9e1f9229f12c29d7f 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 ffa89d930ac08f4338c33cbb6937cbfa45708b29..c0885e8aa4618510a1d8bf40021be35675cd5ad9 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 4a8194813adcefa18479a92f9196ca1c38476f02..4e0d1caaba552a9dccb3d8bcc40831e7d876b153 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 18456043041a0f948730cdeef918187fd1184636..ddc87ac445110d93d3ba8bb8827da8eff93090ca 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 5d5e733a0feba84dd8c6b7f9a64592c32437128d..c47b71a695c93b32b07e75a9e361d038d827a5b9 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 20e4403d6927dd09c423427f076e3f6549147055..3f796b7b68cbda1f16e19c0acf5506155c19fbec 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 41192a42b0f64135a594b48340190a2a3b9bdecc..19893a58588fbfa86f6722cf3eb84501a88b874b 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 219a4b5ba49dddbdd2ba4c80ec1eeed93ae6556a..f21fd3b23becd68d57328b0780b95114b8d13adf 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 34744c396edad37059a870d922c0c36e4ad4f5e0..e1a7c9f2f6303a0208fbe66bc5b5ad07714ac7e0 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 459342b3e52d70dc523914ca61d28b3bc0bf72f6..b2dee548120bb9bdb3258ffb31e529a251ef2eba 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 83118199d3bccc9936a95a7175982dc47e2adb1b..8cf99509899b83b00f24ee11fb837bd04c6fc3dd 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 0aaee693131a371488fc65679d15a407e5a7ef84..25d805cedec0c63556ea527ce1e31cec239735b8 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 41110778b4f0d31307af4e77b4f25afc03ab4fec..92d857ae20de19a758ebc0ff7beea2304f27217d 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 ee356fa7b824e910430b93b4f655e94873967be1..cd0c3f1fa810236d6c904b86c0e433990c63de2e 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 f3ca5134af69ac8d3937ea3ca90630cb372fb96c..c35cea453d2f08d581b6619776b443d72f0e7ecc 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 67bfb1dfcbd736d183c2cb4e6afee86d050e55d5..7b9dc8202e9054b2c19758b224c43c5f1934d393 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 2c6df46a6cee30c76cdcdf35dbbb7e348469846a..d392d4bcaf8e3a3cf24e83d04d7cefca5df3ee81 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 b18bf37df08078ee56b69e09b1be69e4dbfdf9ff..a69e8e66606137b0fc41da39f936c2888264d279 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 c7079a053280fcd8f361eb4c163b754bb27a6ae3..58e5bfeb631b054fa9be469296361c41462dae42 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 cc503b59b74b99ab1e369279cf52b2148d2e3daf..00a5934470da4f705db0b02e45a49044d58a3f53 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 3469e66544b7a2b94a9fb5532fd508a7beeb666a..a7d1c72fc3096148844dcef0eb0b03f99f3acbf6 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 3814435ab05a2def2a1d5992e19e02f5ac0d3dc3..9c1baa3b7973cb87b2b85e77f0805dc06877c792 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 c8c03634c4fe2504cec03ccfeb492be8ec6feb4b..9b02f82f907a48e03e5515a744d13e94e0edea77 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 7f2b9502658a53b02aa500352c3e5e40a452c219..f66d9a8db4b52423cd3d9e34f02218c03cfd65a6 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 5674318eece924cfbc127d2acc212cabed32a430..f0d511002cad460a727dfc0231534731a4250ae3 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 d5814ee041e118a136755cb3662ba442fb45c096..a95cfc671a79ad5b8e3585492f92e3b320494d90 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 e96943d0f0afab5747755f59a9ded9f2a515ac86..54d55d9c57c78ea659e0f401bdf95b068c67e39e 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 dede1fffc519e9eaad91d991d9ed923bc1624095..becdd3b0be0443c16c75215506c1f64311b8d4f1 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 ede4e7701ef061cfd59e5bab25901a5308ba8c74..7d396d540366ad19174eb1a62c3cb4274ba0ae21 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 d4fba835d25d4e60493a886d2babbfe0de8c8a40..168b8120fd8e796271aff7f59f1ab5d1ab359edf 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 c3dfc794ecdcd281e9396ab71d43aff07aec132b..3eb4c0324767f97083fca7e6f5580bcbb6422103 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 83f3ad9310f7fa47c08156a4cecafb60e2d07737..e93484291dc9e05efd105bc828db91388675a333 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。