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README
Apache-2.0

MindSpore Federated

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概述

MindSpore Federated是一款开源联邦学习框架,支持面向千万级无状态终端设备的商用化部署,可在用户数据不出本地的前提下,使能全场景智能应用。

联邦学习是一种加密的分布式机器学习技术,其支持机器学习的各参与方在不直接共享本地数据的前提下,共建AI模型。MindSpore Federated目前优先专注于参与方数量规模较大的横向联邦学习应用场景。

MindSpore Architecture

安装

MindSpore Federated的安装可以采用pip安装或者源码编译安装两种方式。

pip安装

使用pip命令安装,请从MindSpore Federated下载页面下载并安装whl包。

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/Federated/{arch}/mindspore_federated-{version}-{python_version}-linux_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
  • {version}表示MindSpore Federated版本号,例如下载1.1.0版本MindSpore Federated时,{version}应写为1.1.0。
  • {arch}表示系统架构,例如使用的Linux系统是x86架构64位时,{arch}应写为x86_64。如果系统是ARM架构64位,则写为aarch64
  • {python_version}表示用户的Python版本,Python版本为3.7.5时,{python_version}应写为cp37-cp37m。Python版本为3.9.0时,则写为cp39-cp39。请和当前安装的MindSpore Federated使用的Python环境保持一致。

源码编译安装

通过源码编译安装。

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:

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:

from mindspore_federated import FLServerJob

运行样例

后续独立文档。

环境准备

安装和启动Redis服务器

联邦学习默认依赖Redis服务器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。

安装Redis服务器:

sudo apt-get install redis

运行Redis服务器:

redis-server --port 2345 --save ""

安装MindSpore

Worker运行环境:混合模式和云云联邦场景,根据Worker依赖的硬件环境安装相应的MindSpore包。

Server和Scheduler运行环境:只需要安装CPU版本的MindSpore whl,其他硬件版本的MindSpore whl包也能满足需要。

MindSpore版本依赖关系

由于MindSpore Federated与MindSpore有依赖关系,请按照根据下表中所指示的对应关系,在MindSpore下载页面下载并安装对应的whl包。

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{MindSpore-Version}/MindSpore/cpu/ubuntu_x86/mindspore-{MindSpore-Version}-cp37-cp37m-linux_x86_64.whl
MindSpore Federated Version Branch MindSpore version
0.1.0 r0.1 1.8.0
0.1.0 r0.1 1.9.0
0.2.0 r0.1 2.1.0

非混合模式,端云联邦

1、样例路径:

cd tests/st/cross_device_cloud

2、据实际运行需要修改Yaml配置文件:default_yaml_config.yaml

3、运行Server,默认启动1个Server,HTTP服务器地址为127.0.0.1:6666

python run_server.py

可通过指定${http_server_address}设置HTTP Server的IP+端口号,默认为6666,${server_num}为启动的Server个数:

python run_server.py --http_server_address=${http_server_address} --local_server_num=${server_num}

注意可以通过输入checkpoint的路径(指定checkpoint_dir)与构造神经网络的方式传入feature_map,请参考cross_device_femnist目录下的run_cloud.py文件。

4、启动Scheduler,管理面地址默认为127.0.0.1:11202

python run_sched.py

可通过额外指定scheduler_manage_address设定管理面地址,其中${scheduler_manage_address}为Scheduler管理面HTTP服务器的地址。

python run_sched.py --scheduler_manage_address=${scheduler_manage_address}

混合联邦模式

1、样例路径:

cd tests/st/hybrid_cloud

2、据实际运行需要修改Yaml配置文件:default_yaml_config.yaml

3、运行Server,默认启动1个Server,HTTP服务器地址为127.0.0.1:6666

python run_hybrid_train_server.py

可通过指定${http_server_address}设置HTTP Server的IP+端口号,默认为6666,${server_num}为启动的Server个数:

python run_hybrid_train_server.py --http_server_address=${http_server_address} --local_server_num=${server_num}

4、启动Scheduler,管理面地址默认为127.0.0.1:11202

python run_hybrid_train_sched.py

可通过额外指定scheduler_manage_address设定管理面地址,其中${scheduler_manage_address}为Scheduler管理面HTTP服务器的地址。

python run_hybrid_train_sched.py --scheduler_manage_address=${scheduler_manage_address}

5、运行Worker,运行于云侧启动有监督训练,默认启动1个Worker,HTTP服务器地址为127.0.0.1:6666

python run_hybrid_train_worker.py

可通过额外,其中${dataset_path}为训练集数据的路径。

python run_hybrid_train_worker.py  --dataset_path=${dataset_path}

云云联邦模式

1、样例路径:

cd tests/st/cross_silo_femnist

2、据实际运行需要修改Yaml配置文件:default_yaml_config.yaml

3、运行Server,默认启动1个Server,HTTP服务器地址为127.0.0.1:6666

python run_cross_silo_femnist_server.py

可通过指定${http_server_address}设置HTTP Server的IP+端口号,默认为6666,${server_num}为启动的Server个数:

python run_cross_silo_femnist_server.py --http_server_address=${http_server_address} --local_server_num=${server_num}

4、启动Scheduler,管理面地址默认为127.0.0.1:11202

python run_cross_silo_femnist_sched.py

可通过额外指定scheduler_manage_address设定管理面地址,其中${scheduler_manage_address}为Scheduler管理面HTTP服务器的地址。

python run_cross_silo_femnist_sched.py --scheduler_manage_address=${scheduler_manage_address}

5、运行Worker,运行于端侧启动有监督训练,多个worker之间参与联合建模,HTTP服务器地址为127.0.0.1:6666

python run_cross_silo_femnist_worker.py

可通过额外,其中${dataset_path}为训练集数据的路径。

python run_cross_silo_femnist_worker.py  --dataset_path=${dataset_path}

最后:启动客户端Python模拟

bash run_smlt.sh 4 http 127.0.0.1:6666

其中4为启动模拟客户端的数量,http为启动访问的方式,127.0.0.1:6666为服务器server集群的监听地址。

启动ssl通信加密访问模式

1、样例路径:

cd tests/st/cross_device_cloud

2、证书生成样例请运行/tests/ut/python/generate_certs.sh

3、据实际运行需要修改Yaml配置文件:default_yaml_config.yaml,需要设置enable_ssl为true,设置cacert_filename(path/to/ca.crt), cert_filename(/path/to/client.crt), private_key_filename(/path/to/clientkey.pem)

4、以ssl方式运行Redis服务器:

redis-server --port 0 --tls-port 2345 --tls-cert-file /path/to/server.crt --tls-key-file /path/to/serverkey.pem --tls-ca-cert-file /path/to/ca.crt --save ""

5、运行Server,默认启动1个Server,HTTP服务器地址为127.0.0.1:6666, 需要在run_server.py设置秘钥参数对象SSLConfig("server_password", "client_password"), 表示客户端证书秘钥与服务器证书秘钥的密码。

python run_server.py

可通过指定${http_server_address}设置HTTP Server的IP+端口号,默认为6666,${server_num}为启动的Server个数:

python run_server.py --http_server_address=${http_server_address} --local_server_num=${server_num}

注意可以通过输入checkpoint的路径(指定checkpoint_dir)与构造神经网络的方式传入feature_map,请参考cross_device_femnist目录下的run_cloud.py文件。

6、启动Scheduler,管理面地址默认为127.0.0.1:11202

python run_sched.py

7、可通过额外指定scheduler_manage_address设定管理面地址,其中${scheduler_manage_address}为Scheduler管理面HTTP服务器的地址。

python run_sched.py --scheduler_manage_address=${scheduler_manage_address}

最后:启动客户端Python模拟

bash run_smlt.sh 4 https 127.0.0.1:6666

其中4为启动模拟客户端的数量,https为启动访问的方式,127.0.0.1:6666为服务器server集群的监听地址。

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版本说明

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许可证

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