在联邦学习中每次挑选3个客户端形成一个全局模型然后对测试集进行预测，重复这一操做10次形成10个全局模型分别对测试集进行预测代码pytorch代码

以下是一个简单的 PyTorch 代码示例，演示如何在 PySyft 中实现您描述的联邦学习方法：

import torch
import syft as sy
hook = sy.TorchHook(torch)

# 加载您的数据集
train_data, test_data = ...

# 定义客户端数据的分区函数
def client_data_partitioner(dataset, num_clients):
    # 将数据集分成 num_clients 份，每份大小相等
    data_len = len(dataset)
    return [dataset[int(i*data_len/num_clients):int((i+1)*data_len/num_clients)] for i in range(num_clients)]

# 定义联邦数据集
num_clients = ...
train_federated_data = []
for i in range(num_rounds):
    partitions = client_data_partitioner(train_data, num_clients)
    federated_data = []
    for j in range(num_clients):
        client_data = partitions[j]
        client_target = torch.zeros((len(client_data), num_classes))
        client_target[torch.arange(len(client_data)), client_data[:, -1].long()] = 1.
        federated_data.append((client_data[:, :-1], client_target))
    train_federated_data.append(federated_data)

# 定义模型和优化器
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = torch.nn.Linear(input_size, hidden_size)
        self.fc2 = torch.nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = Net()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 定义损失函数和评估指标
loss_fn = torch.nn.MSELoss()
metrics = {'accuracy': lambda predictions, targets: (torch.argmax(predictions, dim=1) == torch.argmax(targets, dim=1)).float().mean()}

# 定义联邦训练循环
def train_on_single_client(client_data, client_target):
    # 在单个客户端上训练模型
    for epoch in range(num_epochs):
        optimizer.zero_grad()
        output = model(client_data)
        loss = loss_fn(output, client_target)
        loss.backward()
        optimizer.step()

    return model

@torch.jit.script
def federated_mean(models):
    # 求平均得到全局模型
    model_sum = models[0].copy()
    for i in range(1, len(models)):
        for param in model_sum.parameters():
            param += models[i].get(param, torch.zeros_like(param))
    for param in model_sum.parameters():
        param /= len(models)
    return model_sum

# 定义联邦评估函数
def evaluate(model, test_data):
    # 在测试集上评估模型
    with torch.no_grad():
        predictions = model(test_data[:, :-1])
        targets = test_data[:, -1]
        loss = loss_fn(predictions, targets)
        accuracy = metrics['accuracy'](predictions, targets)
    return {'loss': loss, 'accuracy': accuracy}

# 运行联邦学习
models = []
for i in range(num_rounds):
    federated_data = train_federated_data[i]
    # 每轮训练选择3个客户端
    client_ids = [j for j in range(num_clients) if j%3 == i%3]
    client_models = []
    for client_id in client_ids:
        client_data, client_target = federated_data[client_id]
        client_model = train_on_single_client(client_data, client_target)
        client_models.append(client_model)
    # 求平均得到全局模型
    global_model = federated_mean(client_models)
    models.append(global_model)
    # 对测试集进行预测
    test_metrics = []
    for test_sample in test_data:
        predictions = global_model(test_sample[:-1].unsqueeze(0))
        targets = test_sample[-1].unsqueeze(0)
        test_metrics.append(evaluate(predictions, targets))
    test_metrics = {'loss': torch.stack([metric['loss'] for metric in test_metrics]).mean(),
                    'accuracy': torch.stack([metric['accuracy'] for metric in test_metrics]).mean()}
    # 输出评估结果
    print('Round {}: metrics={}'.format(i, test_metrics))

这里使用了 PySyft 库，它提供了一种方便的方式来实现联邦学习。您需要根据您的具体情况修改代码中的数据集、模型、优化器、损失函数和评估指标等部分。