使用FATE在两台机器实现横向联邦学习的逻辑回归任务

目录
一、环境准备
二、横向联邦学习
1.数据处理
2.数据上传
3.修改训练用配置文件（在机器B上）
4.提交任务进行训练（在机器B上）
三、模型评估
1.修改配置文件
2.提交任务进行训练
3.查看评估结果
四、删除部署
一、环境准备
关于FATE环境配置可以参考之前的《使用部署多机联邦学习环境（二）》
基本流程：
数据处理数据上传模型训练模型预测二、横向联邦学习 1.数据处理
数据集使用库内置的乳腺癌肿瘤数据集，为了模拟横向联邦学习，将数据集切分为特征相同的横向联邦形式。
该数据集一共有569条数据，30个特征数（其实是10个属性，分别以均值mean、标准差std、最差值worst出现了三次），一个标签（1：良性肿瘤、0：恶行肿瘤，1：0=357：212）。
现将前200个作为机器A的数据，存储为.csv，后269个数据作为机器B的数据，存储为.csv，最后100个数据作为测试数据，存储为.csv 。
在装有所需运行库的环境下运行以下代码，执行后会在当前路径下生成、和三个文件，它们分别是host 、guest 的训练数据集以及验证数据集。
from sklearn.datasets import load_breast_cancerimport pandas as pd breast_dataset = load_breast_cancer()breast = pd.DataFrame(breast_dataset.data, columns=breast_dataset.feature_names)breast = (breast-breast.mean())/(breast.std())#z-score标准化col_names = breast.columns.values.tolist()columns = {}for idx, n in enumerate(col_names):columns[n] = "x%d"%idx breast = breast.rename(columns=columns) breast['y'] = breast_dataset.targetbreast['idx'] = range(breast.shape[0])idx = breast['idx']breast.drop(labels=['idx'], axis=1, inplace = True)breast.insert(0, 'idx', idx)breast = breast.sample(frac=1)#打乱数据train = breast.iloc[:469]eval = breast.iloc[469:]breast_1_train = train.iloc[:200]breast_1_train.to_csv('breast_1_train.csv', index=False, header=True)breast_2_train = train.iloc[200:]breast_2_train.to_csv('breast_2_train.csv', index=False, header=True)eval.to_csv('breast_eval.csv', index=False, header=True)
2.数据上传
首先将数据文件上传到虚拟机中，运行FATE后，在/data//fate中，可以看到集群文件，进入集群（confs-10000）里面的文件夹，这个文件夹是外部与容器中的共享文件夹，进入，在里面创建文件夹，可以将数据上传到这里，使用rz文件上传工具，输入以下命令安装，运行后会弹出文件选择框，选择文件上传即可，将文件在两台机器上都上传一遍。
# sudo apt-get install lrzsz# rz -be
然后进入到容器中
# docker exec -it confs-10000_python_1 bash# cd examples/mydata//刚才上传的数据就在这里
FATE中集成了很多常用的机器学习模型参考，在下的-1.x-中，这次选用逻辑回归模型，即sion 。
首先在机器A上，进入sion中，修改.json，将训练数据1上传到系统中。
# vi upload_data_host.json{//指定数据文件，使用绝对路径不容易出错"file": "/data/projects/fate/python/examples/mydata/breast_1_train.csv","head": 1,"partition": 10,"work_mode": 1,"table_name": "homo_breast_1_train",//指定表名"namespace": "homo_host_breast_train"//指定命名空间}//使用fate_flow上传数据，注意自己的fate_flow的路径是否一致# python ../../../fate_flow/fate_flow_client.py -f upload -c upload_data_host.json //返回输出，可以从board_url中的网址中看到执行结果{"data": {"board_url": "http://fateboard:8080/index.html#/dashboard?job_id=202204200159215834845&role=local&party_id=0","job_dsl_path": "/data/projects/fate/python/jobs/202204200159215834845/job_dsl.json","job_runtime_conf_path": "/data/projects/fate/python/jobs/202204200159215834845/job_runtime_conf.json","logs_directory": "/data/projects/fate/python/logs/202204200159215834845","namespace": "homo_host_breast_train","table_name": "homo_breast_1_train"},"jobId": "202204200159215834845","retcode": 0,"retmsg": "success"}
然后修改.json文件，将测试数据上传到系统中。
# vi upload_data_test.json {"file": "/data/projects/fate/python/examples/mydata/breast_eval.csv","head": 1,"partition": 10,"work_mode": 1,"table_name": "homo_breast_1_eval","namespace": "homo_host_breast_eval"}# python ../../../fate_flow/fate_flow_client.py -f upload -c upload_data_test.json
然后在机器B上，进入sion中，修改.json，将训练数据2上传到系统中。
# vi upload_data_guest.json{"file": "/data/projects/fate/python/examples/mydata/breast_2_train.csv","head": 1,"partition": 10,"work_mode": 1,"table_name": "homo_breast_2_train","namespace": "homo_guest_breast_train"}# python ../../../fate_flow/fate_flow_client.py -f upload -c upload_data_guest.json
然后修改.json文件，将测试数据上传到系统中。
# vi upload_data_test.json {"file": "/data/projects/fate/python/examples/mydata/breast_eval.csv","head": 1,"partition": 10,"work_mode": 1,"table_name": "homo_breast_2_eval","namespace": "homo_guest_breast_eval"}# python ../../../fate_flow/fate_flow_client.py -f upload -c upload_data_test.json
3.修改训练用配置文件（在机器B上）
FATE进行联邦学习需要用到两个配置文件dsl和conf 。
dsl：用来描述任务模块，将任务模块以有向无环图的形式组合在一起
conf：用来设置各个组件的参数，比如输入模块的数据表名、算法模块的学习率、batch大小、迭代次数等。
在sion里面有很多预设文件，挑选其中两个来修改，即.json和b_dsl.json 。

文章插图

# vi test_homolr_train_job_dsl.json//当前dsl中已经定义了三个组件模块，构成了最基本的横向联邦模型流水线，直接使用即可{"components" : {"dataio_0": {"module": "DataIO","input": {"data": {"data": ["args.train_data"]}},"output": {"data": ["train"],"model": ["dataio"]}},"homo_lr_0": {"module": "HomoLR","input": {"data": {"train_data": ["dataio_0.train"]}},"output": {"data": ["train"],"model": ["homolr"]}},"evaluation_0": {"module": "Evaluation","input": {"data": {"data": ["homo_lr_0.train"]}},"output": {"data": ["evaluate"]}}}}

# vi test_homolr_train_job_conf.json {"initiator": {"role": "guest","party_id": 9999//此处修改了guest的id},"job_parameters": {"work_mode": 1//将工作模式设为集群模式},"role": {"guest": [9999],//修改guest的id"host": [10000],"arbiter": [10000]},"role_parameters": {"guest": {"args": {"data": {//修改此处的name和namespace和upload文件中的一致"train_data": [{"name": "homo_breast_2_train", "namespace": "homo_guest_breast_train"}]}}},"host": {"args": {"data": {//修改此处的name和namespace和upload文件中的一致"train_data": [{"name": "homo_breast_1_train", "namespace": "homo_host_breast_train"}]}},"evaluation_0": {"need_run": [false]}}},"algorithm_parameters": {"dataio_0":{"with_label": true,"label_name": "y","label_type": "int","output_format": "dense"},"homo_lr_0": {"penalty": "L2","optimizer": "sgd","eps": 1e-5,"alpha": 0.01,"max_iter": 10,"converge_func": "diff","batch_size": 500,"learning_rate": 0.15,"decay": 1,"decay_sqrt": true,"init_param": {"init_method": "zeros"},"encrypt_param": {"method": "Paillier"},"cv_param": {"n_splits": 4,"shuffle": true,"random_seed": 33,"need_cv": false}}}}

4.提交任务进行训练（在机器B上）

# python ../../../fate_flow/fate_flow_client.py -f submit_job -d test_homolr_train_job_dsl.json -c test_homolr_train_job_conf.json//返回输出，可以从board_url中查看任务运行情况{"data": {"board_url": "http://fateboard:8080/index.html#/dashboard?job_id=202204200254141918057&role=guest&party_id=9999","job_dsl_path": "/data/projects/fate/python/jobs/202204200254141918057/job_dsl.json","job_runtime_conf_path": "/data/projects/fate/python/jobs/202204200254141918057/job_runtime_conf.json","logs_directory": "/data/projects/fate/python/logs/202204200254141918057","model_info": {"model_id": "arbiter-10000#guest-9999#host-10000#model","model_version": "202204200254141918057"}},"jobId": "202204200254141918057","retcode": 0,"retmsg": "success"}

训练时可以从中查看任务运行情况，从中点击view the 可以查看具体训练结果。
三、模型评估
模型评估是机器学习算法设计中的重要一环，在联邦学习场景下也是如此，常用的模型评估方法包括留出法和交叉验证法。
1.修改配置文件
在上面将数据集切分出了一份.csv用于测试（即采用留出法），并上传到了系统中，现在修改配置文件来进行模型的评估。

# vi test_homolr_train_job_dsl.json//编写dsl配置文件{"components" : {"dataio_0": {"module": "DataIO","input": {"data": {"data": ["args.train_data"]}},"output": {"data": ["train"],"model": ["dataio"]}},"dataio_1": {//此处添加dataio_1模块，用于读取测试数据"module": "DataIO","input": {"data": {"data": ["args.eval_data"]},"model": ["dataio_0.dataio"]},"output":{"data":["eval_data"]}},"homo_lr_0": {"module": "HomoLR","input": {"data": {"train_data": ["dataio_0.train"]}},"output": {"data": ["train"],"model": ["homolr"]}},"homo_lr_1": {//此处添加homo_lr_1模块，用于对测试数据进行训练"module": "HomoLR","input": {"data": {"eval_data": ["dataio_1.eval_data"]},"model": ["homo_lr_0.homolr"]},"output": {"data": ["eval_data"],"model": ["homolr"]}},"evaluation_0": {"module": "Evaluation","input": {"data": {"data": ["homo_lr_0.train"]}},"output": {"data": ["evaluate"]}},"evaluation_1": {//此处添加evaluation_1模块，用于对测试数据进行评估"module": "Evaluation","input": {"data": {"data": ["homo_lr_1.eval_data"]}},"output": {"data": ["evaluate"]}}}}

# vi test_homolr_train_job_conf.json//编写conf配置文件{"initiator": {"role": "guest","party_id": 9999},"job_parameters": {"work_mode": 1},"role": {"guest": [9999],"host": [10000],"arbiter": [10000]},"role_parameters": {"guest": {//此处添加guest的测试数据"args": {"data": {"train_data": [{"name": "homo_breast_2_train", "namespace": "homo_guest_breast_train"}]"eval_data": [{"name": "homo_breast_2_eval", "namespace": "homo_guest_breast_eval"}]}}},"host": {//此处添加host的测试数据"args": {"data": {"train_data": [{"name": "homo_breast_1_train", "namespace": "homo_host_breast_train"}]"eval_data": [{"name": "homo_breast_1_eval", "namespace": "homo_host_breast_eval"}]}},"evaluation_0": {"need_run": [false]},"evaluation_1": {//此处添加evaluation_1"need_run": [false]}}},"algorithm_parameters": {"dataio_0":{"with_label": true,"label_name": "y","label_type": "int","output_format": "dense"},"homo_lr_0": {"penalty": "L2","optimizer": "sgd","eps": 1e-5,"alpha": 0.01,"max_iter": 10,"converge_func": "diff","batch_size": 500,"learning_rate": 0.15,"decay": 1,"decay_sqrt": true,"init_param": {"init_method": "zeros"},"encrypt_param": {"method": "Paillier"},"cv_param": {"n_splits": 4,"shuffle": true,"random_seed": 33,"need_cv": false}}}}

2.提交任务进行训练

# python ../../../fate_flow/fate_flow_client.py -f submit_job -d test_homolr_train_job_dsl.json -c test_homolr_train_job_conf.json

3.查看评估结果
四、删除部署
如果需要删除部署，则在部署机器上运行以下命令可以停止所有FATE集群：
# bash docker_deploy.sh --delete all
如果想要彻底删除在运行机器上部署的FATE，可以分别登录节点，然后运行命令：
【使用FATE在两台机器实现横向联邦学习的逻辑回归任务】# cd /data/projects/fate/confs-///此处的ID就是集群的ID# docker-compose down# rm -rf ../confs-/