以联邦学习为基础，训练多个联邦学习分类器代码

好的，以下是一个使用联邦学习训练多个分类器的代码示例：

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch import nn, optim

# 定义模型结构
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 10)
        self.dropout = nn.Dropout(p=0.2)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.view(x.shape[0], -1)
        x = self.dropout(self.relu(self.fc1(x)))
        x = self.dropout(self.relu(self.fc2(x)))
        x = self.fc3(x)
        return x

# 定义联邦学习服务器
class FederatedServer:
    def __init__(self, num_clients, train_data, test_data, lr=0.01, batch_size=64, epochs=10):
        self.num_clients = num_clients
        self.train_data = train_data
        self.test_data = test_data
        self.lr = lr
        self.batch_size = batch_size
        self.epochs = epochs
        self.clients = []
        self.server_model = Model()
        self.criterion = nn.CrossEntropyLoss()
        self.optimizer = optim.SGD(self.server_model.parameters(), lr=self.lr)

        # 初始化客户端
        for i in range(num_clients):
            data_loader = DataLoader(train_data[i], batch_size=self.batch_size, shuffle=True)
            client_model = Model()
            client_optimizer = optim.SGD(client_model.parameters(), lr=self.lr)
            self.clients.append({'data_loader': data_loader, 'model': client_model, 'optimizer': client_optimizer})

    # 训练客户端模型
    def train_client_model(self, client):
        client['model'].train()
        for epoch in range(self.epochs):
            for images, labels in client['data_loader']:
                client['optimizer'].zero_grad()
                output = client['model'](images)
                loss = self.criterion(output, labels)
                loss.backward()
                client['optimizer'].step()

    # 聚合客户端模型
    def aggregate_client_models(self):
        for param in self.server_model.parameters():
            param.data = torch.zeros_like(param.data)
        for client in self.clients:
            for param, client_param in zip(self.server_model.parameters(), client['model'].parameters()):
                param.data += client_param.data / self.num_clients

    # 在测试集上评估模型
    def evaluate_model(self):
        self.server_model.eval()
        test_loss = 0
        test_accuracy = 0
        with torch.no_grad():
            for images, labels in self.test_data:
                output = self.server_model(images)
                test_loss += self.criterion(output, labels)
                ps = torch.exp(output)
                top_p, top_class = ps.topk(1, dim=1)
                equals = top_class == labels.view(*top_class.shape)
                test_accuracy += torch.mean(equals.type(torch.FloatTensor))
        return test_loss / len(self.test_data), test_accuracy / len(self.test_data)

    # 训练联邦模型
    def train(self):
        for epoch in range(self.epochs):
            for client in self.clients:
                self.train_client_model(client)
            self.aggregate_client_models()
            test_loss, test_accuracy = self.evaluate_model()
            print(f"Epoch {epoch+1}/{self.epochs}, Test Loss: {test_loss:.3f}, Test Accuracy: {test_accuracy:.3f}")

这段代码实现了一个简单的联邦学习服务器，其中模型结构为一个三层全连接神经网络，使用 SGD 优化器和交叉熵损失函数。在服务器初始化时，会创建多个客户端，每个客户端使用不同的数据集进行训练。在训练过程中，服务器会通过聚合客户端模型的方式来更新自己的模型参数，并在测试集上评估模型性能。