差分隐私Fedavg代码学习

好的，您可以参考以下代码实现差分隐私的FedAvg算法：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torchvision import datasets, transforms

# 设置超参数
batch_size = 64
lr = 0.01
momentum = 0.5
eps = 0.5  # 噪声扰动参数
delta = 1e-5  # 总体隐私预算

# 定义数据转换
transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

# 加载MNIST数据集
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('data', train=True, download=True, transform=transform),
    batch_size=batch_size, shuffle=True)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

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

# 定义服务器端的模型和优化器
global_model = Net()
global_optimizer = optim.SGD(global_model.parameters(), lr=lr, momentum=momentum)

# 定义客户端的模型和优化器
def client_update(local_model, data, target):
    local_optimizer = optim.SGD(local_model.parameters(), lr=lr, momentum=momentum)
    local_optimizer.zero_grad()
    output = local_model(data)
    loss = nn.functional.cross_entropy(output, target)
    loss.backward()
    local_optimizer.step()
    return local_model.state_dict(), len(data)

# 为FedAvg添加噪声
def add_noise(v):
    noise = np.random.laplace(0, v/eps, 1)
    return v + noise

# 在客户端上运行FedAvg算法
def client_fedavg(local_model, data_loader, round, delta):
    local_weight, local_n = client_update(local_model, data_loader.dataset.data, data_loader.dataset.targets)
    weight_norms = []
    for w in local_weight.values():
        weight_norms.append(np.linalg.norm(w.numpy()))
    norm_clip = min(1, max(0, max(weight_norms)))
    for key in local_weight.keys():
        local_weight[key] = add_noise(local_weight[key] / norm_clip)  # 添加噪声并进行归一化
    return local_weight, local_n

# 运行FedAvg算法
def fedavg(rounds):
    for r in range(rounds):
        global_weight = {}
        global_n = 0
        for idx, (data, target) in enumerate(train_loader):
            local_weight, local_n = client_fedavg(global_model, data_loader[idx], r, delta / len(train_loader))
            for key in local_weight.keys():
                if key not in global_weight:
                    global_weight[key] = local_weight[key] * local_n
                else:
                    global_weight[key] += local_weight[key] * local_n
            global_n += local_n
        for key in global_weight.keys():
            global_weight[key] /= global_n
        global_model.load_state_dict(global_weight)
        print('Round: {} Loss: {:.6f}'.format(r+1, nn.functional.cross_entropy(global_model(data_loader.dataset.data), data_loader.dataset.targets)))

if __name__ == '__main__':
    fedavg(10)

以上是一个简单的差分隐私FedAvg算法的实现，主要思路是在每个客户端的本地模型中添加噪声，然后进行加权平均得到全局模型。具体实现中需要注意的地方在代码注释中都有说明。