paddle2.3-基于联邦学习实现FedAVg算法-CNN

import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import random
import time
import paddle
import paddle.nn as nn
import numpy as np
from paddle.io import Dataset,DataLoader
import paddle.nn.functional as F

mnist_data_train=np.load('data/data2489/train_mnist.npy')
mnist_data_test=np.load('data/data2489/test_mnist.npy')
print('There are {} images for training'.format(len(mnist_data_train)))
print('There are {} images for testing'.format(len(mnist_data_test)))
# 数据和标签分离（便于后续处理）
Label=[int(i[0]) for i in mnist_data_train]
Data=[i[1:] for i in mnist_data_train]

There are 60000 images for training
There are 10000 images for testing

class CNN(nn.Layer):
    def __init__(self):
        super(CNN,self).__init__()
        self.conv1=nn.Conv2D(1,32,5)
        self.relu = nn.ReLU()
        self.pool1=nn.MaxPool2D(kernel_size=2,stride=2)
        self.conv2=nn.Conv2D(32,64,5)
        self.pool2=nn.MaxPool2D(kernel_size=2,stride=2)
        self.fc1=nn.Linear(1024,512)
        self.fc2=nn.Linear(512,10)
        # self.softmax = nn.Softmax()
    def forward(self,inputs):
        x = self.conv1(inputs)
        x = self.relu(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool2(x)
        
        x=paddle.reshape(x,[-1,1024])
        x = self.relu(self.fc1(x))
        y = self.fc2(x)
        return y

# 均匀采样，分配到各个client的数据集都是IID且数量相等的
def IID(dataset, clients):
  num_items_per_client = int(len(dataset)/clients)
  client_dict = {}
  image_idxs = [i for i in range(len(dataset))]
  for i in range(clients):
    client_dict[i] = set(np.random.choice(image_idxs, num_items_per_client, replace=False)) # 为每个client随机选取数据
    image_idxs = list(set(image_idxs) - client_dict[i]) # 将已经选取过的数据去除
    client_dict[i] = list(client_dict[i])

  return client_dict

# 非均匀采样，同时各个client上的数据分布和数量都不同
def NonIID(dataset, clients, total_shards, shards_size, num_shards_per_client):
  shard_idxs = [i for i in range(total_shards)]
  client_dict = {i: np.array([], dtype='int64') for i in range(clients)}
  idxs = np.arange(len(dataset))
  data_labels = Label

  label_idxs = np.vstack((idxs, data_labels)) # 将标签和数据ID堆叠
  label_idxs = label_idxs[:, label_idxs[1,:].argsort()]
  idxs = label_idxs[0,:]

  for i in range(clients):
    rand_set = set(np.random.choice(shard_idxs, num_shards_per_client, replace=False)) 
    shard_idxs = list(set(shard_idxs) - rand_set)

    for rand in rand_set:
      client_dict[i] = np.concatenate((client_dict[i], idxs[rand*shards_size:(rand+1)*shards_size]), axis=0) # 拼接
  
  return client_dict

class MNISTDataset(Dataset):
    def __init__(self, data,label):
        self.data = data
        self.label = label

    def __getitem__(self, idx):
        image=np.array(self.data[idx]).astype('float32')
        image=np.reshape(image,[1,28,28])
        label=np.array(self.label[idx]).astype('int64')
        return image, label

    def __len__(self):
        return len(self.label)

class ClientUpdate(object):
    def __init__(self, data, label, batch_size, learning_rate, epochs):
        dataset = MNISTDataset(data,label)
        self.train_loader = DataLoader(dataset,
                    batch_size=batch_size,
                    shuffle=True,
                    drop_last=True)
        self.learning_rate = learning_rate
        self.epochs = epochs
        
    def train(self, model):
        optimizer=paddle.optimizer.SGD(learning_rate=self.learning_rate,parameters=model.parameters())
        criterion = nn.CrossEntropyLoss(reduction='mean')
        model.train()
        e_loss = []
        for epoch in range(1,self.epochs+1):
            train_loss = []
            for image,label in self.train_loader:
                # image=paddle.to_tensor(image)
                # label=paddle.to_tensor(label.reshape([label.shape[0],1]))
                output=model(image)
                loss= criterion(output,label)
                # print(loss)
                loss.backward()
                optimizer.step()
                optimizer.clear_grad()
                train_loss.append(loss.numpy()[0])
            t_loss=sum(train_loss)/len(train_loss)
            e_loss.append(t_loss)
        total_loss=sum(e_loss)/len(e_loss)
        return model.state_dict(), total_loss

train_x = np.array(Data)
train_y = np.array(Label)

BATCH_SIZE = 32
# 通信轮数
rounds = 100
# client比例
C = 0.1
# clients数量
K = 100
# 每次通信在本地训练的epoch
E = 5
# batch size
batch_size = 10
# 学习率
lr=0.001
# 数据切分
iid_dict = IID(mnist_data_train, 100)

def training(model, rounds, batch_size, lr, ds,L, data_dict, C, K, E, plt_title, plt_color):
    global_weights = model.state_dict()
    train_loss = []
    start = time.time()
    # clients与server之间通信
    for curr_round in range(1, rounds+1):
        w, local_loss = [], []
        m = max(int(C*K), 1) # 随机选取参与更新的clients
        S_t = np.random.choice(range(K), m, replace=False)
        for k in S_t:
            # print(data_dict[k])
            sub_data = ds[data_dict[k]]
            sub_y = L[data_dict[k]]
            local_update = ClientUpdate(sub_data,sub_y, batch_size=batch_size, learning_rate=lr, epochs=E)
            weights, loss = local_update.train(model)
            w.append(weights)
            local_loss.append(loss)

        # 更新global weights
        weights_avg = w[0]
        for k in weights_avg.keys():
            for i in range(1, len(w)):
                # weights_avg[k] += (num[i]/sum(num))*w[i][k]
                weights_avg[k]=weights_avg[k]+w[i][k]   
            weights_avg[k]=weights_avg[k]/len(w)
            global_weights[k].set_value(weights_avg[k])
        # global_weights = weights_avg
        # print(global_weights)
    #模型加载最新的参数
        model.load_dict(global_weights)

        loss_avg = sum(local_loss) / len(local_loss)
        if curr_round % 10 == 0:
            print('Round: {}... \tAverage Loss: {}'.format(curr_round, np.round(loss_avg, 5)))
        train_loss.append(loss_avg)

    end = time.time()
    fig, ax = plt.subplots()
    x_axis = np.arange(1, rounds+1)
    y_axis = np.array(train_loss)
    ax.plot(x_axis, y_axis, 'tab:'+plt_color)

    ax.set(xlabel='Number of Rounds', ylabel='Train Loss',title=plt_title)
    ax.grid()
    fig.savefig(plt_title+'.jpg', format='jpg')
    print("Training Done!")
    print("Total time taken to Train: {}".format(end-start))
  
    return model.state_dict()

#导入模型
mnist_cnn = CNN()
mnist_cnn_iid_trained = training(mnist_cnn, rounds, batch_size, lr, train_x,train_y, iid_dict, C, K, E, "MNIST CNN on IID Dataset", "orange")

Round: 10... 	Average Loss: [0.024]
Round: 20... 	Average Loss: [0.015]
Round: 30... 	Average Loss: [0.008]
Round: 40... 	Average Loss: [0.003]
Round: 50... 	Average Loss: [0.004]
Round: 60... 	Average Loss: [0.002]
Round: 70... 	Average Loss: [0.002]
Round: 80... 	Average Loss: [0.002]
Round: 90... 	Average Loss: [0.001]
Round: 100... 	Average Loss: [0.]
Training Done!
Total time taken to Train: 759.6239657402039