联邦学习FedAvg算法训练卷积神经网络来的代码，使用pytorch

以下是使用PyTorch实现联邦学习FedAvg算法训练卷积神经网络的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import copy

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = nn.functional.log_softmax(x, dim=1)
        return output

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.functional.nll_loss(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)

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

def train_federated(model, train_loaders, test_loader, num_rounds, fraction):
    global_model = CNN().to(device)
    
    for round in range(num_rounds):
        local_models = []
        for i in range(len(train_loaders)):
            local_model = copy.deepcopy(global_model)
            optimizer = optim.SGD(local_model.parameters(), lr=0.01)
            train(local_model, device, train_loaders[i], optimizer, round)
            local_models.append(local_model)

        global_dict = global_model.state_dict()
        for key in global_dict.keys():
            global_dict[key] = torch.stack([local_models[i].state_dict()[key] for i in range(len(local_models))]).mean(0)

        global_model.load_state_dict(global_dict)
        test(global_model, device, test_loader)

if __name__ == '__main__':
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # load data
    train_dataset = torch.utils.data.TensorDataset(torch.randn(60000, 1, 28, 28), torch.randint(0, 10, (60000,)))
    test_dataset = torch.utils.data.TensorDataset(torch.randn(10000, 1, 28, 28), torch.randint(0, 10, (10000,)))
    num_clients = 10
    batch_size = 64
    train_loaders = []
    for i in range(num_clients):
        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
        train_loaders.append(train_loader)
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)

    # train federated model
    model = CNN().to(device)
    train_federated(model, train_loaders, test_loader, num_rounds=10, fraction=0.1)

在上面的代码中，我们定义了一个简单的卷积神经网络模型`CNN`，并使用PyTorch内置的优化器`optim.SGD`来进行训练。在`train_federated`函数中，我们执行多轮联邦学习，每轮中每个客户端使用自己的数据进行训练，并将训练得到的本地模型上传至服务器。服务器上使用FedAvg算法对所有本地模型进行平均，并更新全局模型。最后在测试集上评估全局模型的性能。