mnist数据集，给出完整的联邦学习代码

以下是一个基于PyTorch的联邦学习代码示例，用于在MNIST数据集上训练模型：

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

# 定义全局变量
global_model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10)
)

# 定义客户端模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 10)

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

# 训练客户端模型
def train_client(client_model, train_loader, learning_rate, num_epochs):
    optimizer = optim.SGD(client_model.parameters(), lr=learning_rate, momentum=0.5)
    criterion = nn.CrossEntropyLoss()

    for epoch in range(num_epochs):
        for data, target in train_loader:
            optimizer.zero_grad()
            output = client_model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()

    return client_model.state_dict()

# 合并客户端模型
def aggregate_models(client_models):
    global_dict = global_model.state_dict()
    for key in global_dict.keys():
        global_dict[key] = torch.stack([client_models[i][key].float() for i in range(len(client_models))], 0).mean(0)
    global_model.load_state_dict(global_dict)

# 客户端训练函数
def client_train(client_id, train_data, learning_rate, num_epochs):
    client_model = Net()
    client_train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
    client_state_dict = train_client(client_model, client_train_loader, learning_rate, num_epochs)
    return client_state_dict

# 服务器端训练函数
def server_train(train_data, test_data, num_clients, learning_rate, num_epochs):
    global global_model
    client_models = []
    for i in range(num_clients):
        client_data = train_data[i]
        client_state_dict = client_train(i, client_data, learning_rate, num_epochs)
        client_models.append(client_state_dict)
    aggregate_models(client_models)

    # 评估模型
    global_model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in test_data:
            output = global_model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()

    accuracy = 100 * correct / total
    print('Accuracy: {:.2f}%'.format(accuracy))

# 加载数据
train_data = []
for i in range(10):
    dataset = datasets.MNIST('./data', train=True, download=True,
                             transform=transforms.Compose([
                                 transforms.ToTensor(),
                                 transforms.Normalize((0.1307,), (0.3081,))
                             ]))
    train_data.append(torch.utils.data.Subset(dataset, [j for j in range(len(dataset)) if dataset[j][1] == i]))

test_data = datasets.MNIST('./data', train=False, download=True,
                           transform=transforms.Compose([
                               transforms.ToTensor(),
                               transforms.Normalize((0.1307,), (0.3081,))
                           ]))

# 训练模型
num_clients = 10
learning_rate = 0.01
num_epochs = 10

for epoch in range(5):
    print('Epoch {}...'.format(epoch+1))
    server_train(train_data, test_data, num_clients, learning_rate, num_epochs)

这个代码可以运行在一个包含10个客户端的联邦学习系统中，每个客户端使用一个简单的前馈神经网络进行训练，最后在服务器端对所有客户端的模型进行加权平均以获得全局模型。