运用Pysyft框架syft版本0.2.4，torchvision版本0.5.0，torch版本1.4.0，以https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv作为数据集，编写一个联邦学习差分隐私保护的线性回归模型

首先，我们需要安装Pysyft框架，可以通过以下代码进行安装：

!pip install syft==0.2.4

然后，我们需要导入所需的库和模块：

import torch
import syft as sy
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from torch import nn, optim
from torch.utils.data import TensorDataset, DataLoader

接下来，我们需要加载数据集并将其分为两个子集：

data = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv')
features = data.drop('price', axis=1)
target = data.price

X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=123)

然后，我们需要将数据转换为PyTorch张量并将其发送给不同的工作机器：

hook = sy.TorchHook(torch)
alice = sy.VirtualWorker(hook, id="alice")
bob = sy.VirtualWorker(hook, id="bob")

X_train_alice = torch.tensor(np.array(X_train.loc[X_train.cut == "Ideal"]))
y_train_alice = torch.tensor(np.array(y_train.loc[X_train.cut == "Ideal"]))

X_train_bob = torch.tensor(np.array(X_train.loc[X_train.cut != "Ideal"]))
y_train_bob = torch.tensor(np.array(y_train.loc[X_train.cut != "Ideal"]))

X_test = torch.tensor(np.array(X_test))
y_test = torch.tensor(np.array(y_test))

X_train_alice = X_train_alice.send(alice)
y_train_alice = y_train_alice.send(alice)

X_train_bob = X_train_bob.send(bob)
y_train_bob = y_train_bob.send(bob)

X_test = X_test.send(bob)
y_test = y_test.send(bob)

接下来，我们需要定义模型、损失函数和优化器：

class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(8, 1)

    def forward(self, x):
        out = self.linear(x)
        return out


model_alice = LinearRegression()
model_bob = LinearRegression()
criterion = nn.MSELoss()
optimizer_alice = optim.SGD(model_alice.parameters(), lr=0.01)
optimizer_bob = optim.SGD(model_bob.parameters(), lr=0.01)

然后，我们需要定义一个函数来训练模型，并使用差分隐私技术保护模型训练中的隐私：

def train(model, optimizer, train_data, train_labels, epochs, batch_size, noise_multiplier):
    for epoch in range(epochs):
        for i in range(0, len(train_data), batch_size):
            optimizer.zero_grad()
            batch_data = train_data[i:i+batch_size].float()
            batch_labels = train_labels[i:i+batch_size].float()
            preds = model(batch_data)
            loss = criterion(preds, batch_labels)
            loss.backward()
            # Add noise to the gradients
            for param in model.parameters():
                noise = torch.Tensor(param.grad.shape).normal_(0, noise_multiplier)
                param.grad += noise
            optimizer.step()
        print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item()}')

最后，我们需要训练模型并评估其性能：

epochs = 10
batch_size = 32
noise_multiplier = 0.1

train(model_alice, optimizer_alice, X_train_alice, y_train_alice, epochs, batch_size, noise_multiplier)
train(model_bob, optimizer_bob, X_train_bob, y_train_bob, epochs, batch_size, noise_multiplier)

model_alice.move(bob)
model_bob.move(bob)

preds_alice = model_alice(X_test.float())
preds_bob = model_bob(X_test.float())

preds = (preds_alice + preds_bob) / 2

loss = criterion(preds, y_test.float())
print(f'Test Loss: {loss.item()}')

这就是一个使用Pysyft框架实现联邦学习差分隐私保护的线性回归模型的完整代码。