用pytorch写一个使用NSL-KDD数据集训练5层一维卷积神经网络的联邦学习代码。每行代码给出注释。从数据预处理开始写起

# 导入所需的库和模块
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader

# 定义超参数
num_clients = 10 # 客户端个数
num_epochs = 10 # 训练轮数
batch_size = 64 # 每批次训练的数据量
lr = 0.01 # 学习率

# 加载NSL-KDD数据集，分成训练集和测试集
train_data = torch.load("train_data.pt")
train_labels = torch.load("train_labels.pt")
test_data = torch.load("test_data.pt")
test_labels = torch.load("test_labels.pt")

# 数据预处理，将数据分成num_clients份，每份数据量相同
num_samples = train_data.shape[0]
samples_per_client = num_samples // num_clients
train_data_split = []
train_labels_split = []
for i in range(num_clients):
start_idx = i * samples_per_client
end_idx = start_idx + samples_per_client
train_data_split.append(train_data[start_idx:end_idx])
train_labels_split.append(train_labels[start_idx:end_idx])

# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv1d(128, 1, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)

def forward(self, x):
x = self.relu(self.conv1(x))
x = self.relu(self.conv2(x))
x = self.relu(self.conv3(x))
x = self.relu(self.conv4(x))
x = self.conv5(x)
out = x.view(x.size(0), -1) # 将输出展平
return out

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=lr)

# 训练联邦学习模型
for epoch in range(num_epochs):
# 每轮训练前将模型参数从服务器发送到所有客户端
for client_idx in range(num_clients):
client_data = train_data_split[client_idx]
client_labels = train_labels_split[client_idx]
net.load_state_dict(server_params_dict)

# 创建TensorDataset对象和DataLoader对象
train_dataset = TensorDataset(client_data, client_labels)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 训练模型
for batch_idx, (data, labels) in enumerate(train_loader):
data = data.unsqueeze(1) # 增加一个维度，与模型输入维度匹配
optimizer.zero_grad()
output = net(data)
loss = criterion(output, labels)
loss.backward()
optimizer.step()

# 将客户端模型参数发送到服务器
server_params_dict[client_idx] = net.state_dict()

# 服务器进行模型聚合，更新模型参数
for param_idx in range(len(server_params_dict)):
params = [server_params_dict[i][param_idx] for i in range(num_clients)]
server_params_dict[param_idx] = sum(params) / num_clients
net.load_state_dict(server_params_dict)