以联邦学习为基础，训练多个联邦学习分类器代码

以下是一个简单的示例代码，演示如何以联邦学习为基础，训练多个联邦学习分类器：

import torch
import torch.nn as nn
import torch.optim as optim
import syft as sy

hook = sy.TorchHook(torch)

# 创建虚拟客户端
client1 = sy.VirtualWorker(hook, id="client1")
client2 = sy.VirtualWorker(hook, id="client2")
client3 = sy.VirtualWorker(hook, id="client3")

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 客户端训练函数
def train_on_client(client, model, optimizer, train_loader, epochs):
    model.train()
    for epoch in range(epochs):
        for batch_idx, (data, target) in enumerate(train_loader):
            model.send(client)
            data, target = data.to(client), target.to(client)
            optimizer.zero_grad()
            output = model(data)
            loss = nn.functional.cross_entropy(output, target)
            loss.backward()
            optimizer.step()
            model.get()

# 创建数据加载器
train_loader1 = torch.utils.data.DataLoader(dataset1, batch_size=64)
train_loader2 = torch.utils.data.DataLoader(dataset2, batch_size=64)
train_loader3 = torch.utils.data.DataLoader(dataset3, batch_size=64)

# 创建客户端模型
client_model1 = Net()
client_model2 = Net()
client_model3 = Net()

# 客户端初始化
client_init(client_model1)
client_init(client_model2)
client_init(client_model3)

# 创建服务器模型
server_model = Net()

# 服务器初始化
server_init(server_model)

# 定义优化器
optimizer = optim.SGD(server_model.parameters(), lr=0.01)

# 客户端训练
train_on_client(client1, client_model1, optimizer, train_loader1, epochs=5)
train_on_client(client2, client_model2, optimizer, train_loader2, epochs=5)
train_on_client(client3, client_model3, optimizer, train_loader3, epochs=5)

# 聚合客户端模型
models = [client_model1, client_model2, client_model3]
aggregated_model = average_models(models)

# 服务器更新
server_model.load_state_dict(aggregated_model.state_dict())

# 测试服务器模型
server_model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
    for data, target in test_loader:
        output = server_model(data)
        test_loss += nn.functional.cross_entropy(output, target, reduction='sum').item()
        pred = output.argmax(dim=1, keepdim=True)
        correct += pred.eq(target.view_as(pred)).sum().item()

test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)

print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
    test_loss, correct, len(test_loader.dataset), accuracy))

在上面的代码中，我们首先创建了三个虚拟客户端，然后定义了一个简单的神经网络模型。接着，我们创建了三个数据加载器，分别加载三个不同的数据集。然后，我们分别在三个客户端上创建了一个模型，并对其进行了初始化。接下来，我们定义了一个优化器，并使用 `train_on_client` 函数在三个客户端上进行训练。在训练完成后，我们将三个客户端的模型聚合成一个模型，并用它来更新服务器上的模型。最后，我们使用测试数据集对服务器模型进行测试，以评估其性能。