给我一段用DermaMNIST进行联邦学习的案例代码

当然，我可以为你提供一个基于DermaMNIST的联邦学习案例代码，请看以下代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset

from dataloader import load_derma_mnist
from models import CNN

class Client():
    def __init__(self, model=None, train_dataset=None, test_dataset=None, lr=0.1):
        self.model = model
        self.train_dataset = train_dataset
        self.test_dataset = test_dataset
        self.optimizer = optim.SGD(model.parameters(), lr=lr)
    
    def train(self, epochs=10, batch_size=32):
        self.model.train()
        train_loader = DataLoader(self.train_dataset, batch_size=batch_size, shuffle=True)
        criterion = nn.CrossEntropyLoss()
        for epoch in range(epochs):
            running_loss = 0.0
            for i, (images, labels) in enumerate(train_loader):
                self.optimizer.zero_grad()
                outputs = self.model(images)
                loss = criterion(outputs, labels)
                loss.backward()
                self.optimizer.step()
                running_loss += loss.item()
            print('Epoch {} loss: {:.3f}'.format(epoch+1, running_loss/(i+1)))
    
    def test(self, batch_size=32):
        self.model.eval()
        test_loader = DataLoader(self.test_dataset, batch_size=batch_size)
        correct = 0
        with torch.no_grad():
            for images, labels in test_loader:
                outputs = self.model(images)
                _, predicted = torch.max(outputs.data, 1)
                correct += (predicted == labels).sum().item()
        acc = 100 * correct / len(self.test_dataset)
        print('Accuracy: {:.2f}%'.format(acc))
        return acc

def get_client_datasets(dataset, clients=10, samples=200):
    indices = np.arange(len(dataset))
    datasets = []
    for i in range(clients):
        client_indices = np.random.choice(indices, samples, replace=False)
        client_dataset = Subset(dataset, client_indices)
        datasets.append(client_dataset)
    return datasets

if __name__ == '__main__':
    train_dataset, test_dataset = load_derma_mnist()
    client_datasets = get_client_datasets(train_dataset)
    models = [CNN() for i in range(len(client_datasets))]
    lr = 0.01
    epochs = 10
    batch_size = 32
    clients = [Client(models[i], client_datasets[i], test_dataset, lr) for i in range(len(client_datasets))]
    
    for epoch in range(epochs):
        print('Epoch', epoch+1)
        for i, client in enumerate(clients):
            print('Client', i+1, 'training')
            client.train(batch_size=batch_size)
    
    avg_model = CNN()
    for i, client in enumerate(clients):
        avg_model += client.model
    avg_model /= len(clients)
    
    print('Average model accuracy')
    client = Client(avg_model, train_dataset, test_dataset, lr)
    client.test(batch_size=batch_size)

注意：上述代码的数据加载器、数据预处理方式和模型在本地环境（不同的机器）可能会有所不同，需要根据具体情况进行修改。