联邦学习模块

federated_learning/res18Train.py

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+import argparse
+import torch
+import os
+from torch import optim
+from torch.optim import lr_scheduler
+from util.data_utils import get_data
+from util.model_utils import get_model
+from util.train_utils import train_model, validate_model, update_model_weights, v3_update_model_weights
+
+
+def main(args):
+    device = torch.device(args.device)
+    
+    # 数据加载器
+    loader1, loader2, loader3, subset_len, val_loader = get_data(
+        args.train_path, args.val_path, args.batch_size, args.number_workers
+    )
+    
+    # 模型 get_model(name='ResNet', number_class=2, device=device, resnet_type='resnet18')
+    model_a = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
+    model_b = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
+    model_c = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
+    # 添加全局模型
+    global_model = get_model(args.model_name, args.number_class, device, args.deep_backbone).to(device)
+    
+    if args.resume_training:
+        model_a.load_state_dict(torch.load(os.path.join(args.save_dir, 'best_model_a.pth')))
+        model_b.load_state_dict(torch.load(os.path.join(args.save_dir, 'best_model_b.pth')))
+        model_c.load_state_dict(torch.load(os.path.join(args.save_dir, 'best_model_c.pth')))
+        print("已加载之前保存的模型参数继续训练")
+    
+    # 优化器和损失函数
+    criterion = torch.nn.BCEWithLogitsLoss().to(device)
+    
+    optimizer_a = optim.Adam(model_a.parameters(), lr=args.lr, weight_decay=5e-4)
+    optimizer_b = optim.Adam(model_b.parameters(), lr=args.lr, weight_decay=5e-4)
+    optimizer_c = optim.Adam(model_c.parameters(), lr=args.lr, weight_decay=5e-4)
+    scheduler_a = lr_scheduler.ReduceLROnPlateau(optimizer_a, mode='min', factor=0.5, patience=2, verbose=True)
+    scheduler_b = lr_scheduler.ReduceLROnPlateau(optimizer_b, mode='min', factor=0.5, patience=2, verbose=True)
+    scheduler_c = lr_scheduler.ReduceLROnPlateau(optimizer_c, mode='min', factor=0.5, patience=2, verbose=True)
+    
+    # 初始化最优验证损失和模型路径
+    best_val_loss_a = float('inf')
+    best_val_loss_b = float('inf')
+    best_val_loss_c = float('inf')
+    
+    save_dir = args.save_dir
+    os.makedirs(save_dir, exist_ok=True)
+    
+    # 训练与验证
+    for epoch in range(args.epochs):
+        print(f'Epoch {epoch + 1}/{args.epochs}')
+        
+        # 训练模型
+        loss_a = train_model(device, model_a, loader1, optimizer_a, criterion, epoch, 'model_a')
+        loss_b = train_model(device, model_b, loader2, optimizer_b, criterion, epoch, 'model_b')
+        loss_c = train_model(device, model_c, loader3, optimizer_c, criterion, epoch, 'model_c')
+        
+        # 验证模型
+        val_loss_a, val_acc_a, val_auc_a = validate_model(device, model_a, val_loader, criterion, epoch, 'model_a')
+        val_loss_b, val_acc_b, val_auc_b = validate_model(device, model_b, val_loader, criterion, epoch, 'model_b')
+        val_loss_c, val_acc_c, val_auc_c = validate_model(device, model_c, val_loader, criterion, epoch, 'model_c')
+        
+        if args.save_model and val_loss_a < best_val_loss_a:
+            best_val_loss_a = val_loss_a
+            torch.save(model_a.state_dict(), os.path.join(save_dir, 'best_model_a.pth'))
+            print(f"Best model_a saved with val_loss: {best_val_loss_a:.4f}")
+        
+        if args.save_model and val_loss_b < best_val_loss_b:
+            best_val_loss_b = val_loss_b
+            torch.save(model_b.state_dict(), os.path.join(save_dir, 'best_model_b.pth'))
+            print(f"Best model_b saved with val_loss: {best_val_loss_b:.4f}")
+        
+        if args.save_model and val_loss_c < best_val_loss_c:
+            best_val_loss_c = val_loss_c
+            torch.save(model_c.state_dict(), os.path.join(save_dir, 'best_model_c.pth'))
+            print(f"Best model_c saved with val_loss: {best_val_loss_c:.4f}")
+        
+        print(
+            f'Model A - Loss: {loss_a:.4f}, Val Loss: {val_loss_a:.4f}, Val Acc: {val_acc_a:.4f}, AUC: {val_auc_a:.4f}')
+        print(
+            f'Model B - Loss: {loss_b:.4f}, Val Loss: {val_loss_b:.4f}, Val Acc: {val_acc_b:.4f}, AUC: {val_auc_b:.4f}')
+        print(
+            f'Model C - Loss: {loss_c:.4f}, Val Loss: {val_loss_c:.4f}, Val Acc: {val_acc_c:.4f}, AUC: {val_auc_c:.4f}')
+        
+        # 更新模型 A 的权重，每 3 轮 1
+        val_acc_a, val_auc_a, val_acc_a_threshold = v3_update_model_weights(
+            epoch=epoch,
+            model_to_update=model_a,
+            other_models=[model_a, model_b, model_c],
+            global_model=global_model,
+            losses=[loss_a, loss_b, loss_c],
+            val_loader=val_loader,
+            device=device,
+            val_auc_threshold=val_auc_a,
+            validate_model=validate_model,
+            criterion=criterion,
+            update_frequency=1
+        )
+        
+        # 更新模型 B 的权重，每 5 轮1
+        val_acc_b, val_auc_b, val_acc_b_threshold = v3_update_model_weights(
+            epoch=epoch,
+            model_to_update=model_b,
+            other_models=[model_a, model_b, model_c],
+            global_model=global_model,
+            losses=[loss_a, loss_b, loss_c],
+            val_loader=val_loader,
+            device=device,
+            val_auc_threshold=val_auc_b,
+            validate_model=validate_model,
+            criterion=criterion,
+            update_frequency=1
+        )
+        
+        # 更新模型 C 的权重，每 2 轮 1
+        val_acc_c, val_auc_c, val_acc_c_threshold = v3_update_model_weights(
+            epoch=epoch,
+            model_to_update=model_c,
+            other_models=[model_a, model_b, model_c],
+            global_model=global_model,
+            losses=[loss_a, loss_b, loss_c],
+            val_loader=val_loader,
+            device=device,
+            val_auc_threshold=val_auc_c,
+            validate_model=validate_model,
+            criterion=criterion,
+            update_frequency=1
+        )
+    
+    print("Training complete! Best models saved.")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_name', type=str, default='resnet18_psa', help='Model name')
+    parser.add_argument('--deep_backbone', type=str, default='*', help='deeplab backbone')
+    parser.add_argument('--train_path', type=str, default='/media/terminator/实验&代码/yhs/FF++/c40/total/train')
+    parser.add_argument('--val_path', type=str, default='/media/terminator/实验&代码/yhs/FF++/c40/total/val')
+    # parser.add_argument('--train_path', type=str, default='/media/terminator/实验&代码/yhs/FF++_mask_sample/c23/df/train')
+    # parser.add_argument('--val_path', type=str, default='/media/terminator/实验&代码/yhs/FF++_mask_sample/c23/df/val')
+    parser.add_argument('--epochs', type=int, default=10)
+    parser.add_argument('--batch_size', type=int, default=16)
+    parser.add_argument('--number_workers', type=int, default=8)
+    parser.add_argument('--number_class', type=int, default=1)
+    parser.add_argument('--device', type=str, default='cuda:0')
+    parser.add_argument('--lr', type=float, default=0.00005)
+    parser.add_argument('--save_dir', type=str,
+                        default='/media/terminator/实验&代码/yhs/output/work2/resnet18_psa/c40/total/e10',
+                        help='Directory to save best models')
+    parser.add_argument('--save_model', type=bool, default=True, help='是否保存最优模型')
+    parser.add_argument('--resume_training', type=bool, default=False, help='是否从保存的模型参数继续训练')
+    args = parser.parse_args()
+    
+    main(args)

federated_learning/utils/data_utils.py

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+import os
+from PIL import Image
+import torch
+from torchvision import transforms
+from torch.utils.data import DataLoader, Dataset, random_split
+from collections import Counter
+from torch.utils.data import DataLoader, Subset
+from torchvision import transforms, datasets
+import os
+from sklearn.model_selection import train_test_split
+
+
+class CustomImageDataset(Dataset):
+    def __init__(self, root_dir, transform=None):
+        self.root_dir = root_dir
+        self.transform = transform
+        self.image_paths = []
+        self.labels = []
+        
+        # 遍历 root_dir 下的子文件夹 0 和 1
+        for label in [0, 1]:
+            folder_path = os.path.join(root_dir, str(label))
+            if os.path.isdir(folder_path):
+                for img_name in os.listdir(folder_path):
+                    img_path = os.path.join(folder_path, img_name)
+                    self.image_paths.append(img_path)
+                    self.labels.append(label)
+        
+        # 打印用于调试的图像路径和标签
+        # print("Loaded image paths and labels:")
+        # for path, label in zip(self.image_paths[:5], self.labels[:5]):
+        #     print(f"Path: {path}, Label: {label}")
+        # print(f"Total samples: {len(self.image_paths)}\n")
+    
+    def __len__(self):
+        return len(self.image_paths)
+    
+    def __getitem__(self, idx):
+        img_path = self.image_paths[idx]
+        label = self.labels[idx]
+        image = Image.open(img_path).convert("RGB")
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        return image, label
+
+
+def get_test_data(test_image_path, batch_size, nw):
+    data_transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    
+    # test_dataset = datasets.ImageFolder(root=test_image_path, transform=data_transform)
+    
+    test_dataset = CustomImageDataset(root_dir=test_image_path, transform=data_transform)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=nw)
+    return test_loader
+
+
+def get_Onedata(train_image_path, val_image_path, batch_size, num_workers):
+    """
+    加载完整的训练数据集和验证数据集。
+    """
+    data_transform = {
+        "train": transforms.Compose([
+            transforms.Resize((256, 256)),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+        "val": transforms.Compose([
+            transforms.Resize((256, 256)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+    }
+    
+    # 创建训练和验证数据集
+    train_dataset = CustomImageDataset(root_dir=train_image_path, transform=data_transform["train"])
+    val_dataset = CustomImageDataset(root_dir=val_image_path, transform=data_transform["val"])
+    
+    # 创建数据加载器
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+    
+    return train_loader, val_loader
+
+
+def get_data(train_image_path, val_image_path, batch_size, num_workers):
+    data_transform = {
+        "train": transforms.Compose([
+            transforms.Resize((256, 256)),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+        "val": transforms.Compose([
+            transforms.Resize((256, 256)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+        "test": transforms.Compose([
+            transforms.Resize((256, 256)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+    }
+    
+    train_dataset = CustomImageDataset(root_dir=train_image_path, transform=data_transform["train"])
+    val_dataset = CustomImageDataset(root_dir=val_image_path, transform=data_transform["val"])
+    
+    # 切分数据集为三个等分
+    train_len = (len(train_dataset) // 3) * 3
+    train_dataset_truncated = torch.utils.data.Subset(train_dataset, range(train_len))
+    subset_len = train_len // 3
+    dataset1, dataset2, dataset3 = random_split(train_dataset_truncated, [subset_len] * 3)
+    
+    loader1 = DataLoader(dataset1, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    loader2 = DataLoader(dataset2, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    loader3 = DataLoader(dataset3, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+    
+    return loader1, loader2, loader3, subset_len, val_loader
+
+
+def get_Fourdata(train_path, val_path, batch_size, num_workers):
+    """
+    加载训练集和验证集。
+    包括 4 个客户端验证集（df、f2f、fs、nt）和 1 个全局验证集。
+
+    Args:
+        train_path (str): 训练数据路径
+        val_path (str): 验证数据路径
+        batch_size (int): 批量大小
+        num_workers (int): DataLoader 的工作线程数
+
+    Returns:
+        tuple: 包含 4 个客户端训练和验证加载器，以及全局验证加载器
+    """
+    # 数据预处理
+    train_transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    
+    val_transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    
+    # 定义 4 个客户端数据集路径
+    client_names = ['df', 'f2f', 'fs', 'nt']
+    client_train_loaders = []
+    client_val_loaders = []
+    
+    for client in client_names:
+        client_train_path = os.path.join(train_path, client)
+        client_val_path = os.path.join(val_path, client)
+        
+        # 加载客户端训练数据
+        train_dataset = datasets.ImageFolder(root=client_train_path, transform=train_transform)
+        train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+        
+        # 加载客户端验证数据
+        val_dataset = datasets.ImageFolder(root=client_val_path, transform=val_transform)
+        val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+        
+        client_train_loaders.append(train_loader)
+        client_val_loaders.append(val_loader)
+    
+    # 全局验证集
+    global_val_dataset = datasets.ImageFolder(root=val_path, transform=val_transform)
+    global_val_loader = DataLoader(global_val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+    
+    return (*client_train_loaders, *client_val_loaders, global_val_loader)
+
+
+def main():
+    # 设置参数
+    train_image_path = "/media/terminator/实验&代码/yhs/FF++_mask/c23/f2f/train"
+    val_image_path = "/media/terminator/实验&代码/yhs/FF++_mask/c23/f2f/val"
+    batch_size = 4
+    num_workers = 2
+    
+    # 获取数据加载器
+    loader1, loader2, loader3, subset_len, val_loader = get_data(train_image_path, val_image_path, batch_size,
+                                                                 num_workers)
+    
+    # 统计标签数量和类型
+    train_labels = []
+    for dataset in [loader1, loader2, loader3]:
+        for _, labels in dataset:
+            train_labels.extend(labels.tolist())
+    
+    val_labels = []
+    for _, labels in val_loader:
+        val_labels.extend(labels.tolist())
+    
+    # 使用 Counter 统计标签数量
+    train_label_counts = Counter(train_labels)
+    val_label_counts = Counter(val_labels)
+    
+    # 打印统计结果
+    print("Training Dataset - Label Counts:", train_label_counts)
+    print("Validation Dataset - Label Counts:", val_label_counts)
+    print("Label Types in Training:", set(train_labels))
+    print("Label Types in Validation:", set(val_labels))
+
+
+if __name__ == "__main__":
+    main()

federated_learning/utils/model_utils.py

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+import torch
+from torch import nn
+from torchvision import models
+
+from Deeplab.deeplab import DeepLab_F
+from Deeplab.resnet_psa import BasicBlockWithPSA
+from Deeplab.resnet_psa_v2 import ResNet
+from model_base.efNet_base_model import DeepLab
+from model_base.efficientnet import EfficientNet
+from model_base.resnet_more import CustomResNet
+from model_base.xcption import Xception
+
+
+def get_model(name, number_class, device, backbone):
+    """
+    根据指定的模型名称加载模型，并根据任务类别数调整最后的分类层。
+
+    Args:
+        name (str): 模型名称 ('Vgg', 'ResNet', 'EfficientNet', 'Xception')。
+        number_class (int): 分类类别数。
+        device (torch.device): 设备 ('cuda' or 'cpu')。
+        resnet_type (str): ResNet类型 ('resnet18', 'resnet34', 'resnet50', 'resnet101', etc.)。
+
+    Returns:
+        nn.Module: 经过修改的模型。
+    """
+    if name == 'Vgg':
+        model = models.vgg16_bn(pretrained=True).to(device)
+        model.classifier[6] = nn.Linear(model.classifier[6].in_features, number_class)
+    elif name == 'ResNet18':
+        model = CustomResNet(resnet_type='resnet18', num_classes=number_class, pretrained=True).to(device)
+    elif name == 'ResNet34':
+        model = CustomResNet(resnet_type='resnet34', num_classes=number_class, pretrained=True).to(device)
+    elif name == 'ResNet50':
+        model = CustomResNet(resnet_type='resnet50', num_classes=number_class, pretrained=True).to(device)
+    elif name == 'ResNet101':
+        model = CustomResNet(resnet_type='resnet101', num_classes=number_class, pretrained=True).to(device)
+    elif name == 'ResNet152':
+        model = CustomResNet(resnet_type='resnet152', num_classes=number_class, pretrained=True).to(device)
+    elif name == 'EfficientNet':
+        # 使用自定义的 DeepLab 类加载 EfficientNet
+        model = DeepLab(backbone='efficientnet', num_classes=number_class).to(device)
+    elif name == 'Xception':
+        model = Xception(
+            in_planes=3,
+            num_classes=number_class,
+            pretrained=True,
+            pretrained_path="/home/terminator/1325/yhs/fedLeaning/pre_model/xception-43020ad28.pth"
+        ).to(device)
+    elif name == 'DeepLab':
+        # 使用自定义的 DeepLab 类加载 EfficientNet
+        model = DeepLab_F(num_classes=1, backbone=backbone).to(device)
+    elif name == 'resnet18_psa':
+        model = ResNet(BasicBlockWithPSA, [2, 2, 2, 2], number_class)
+    else:
+        raise ValueError(f"Model {name} is not supported.")
+    return model

federated_learning/utils/train_utils.py

@@ -0,0 +1,368 @@
+import numpy as np
+import torch
+from tqdm import tqdm
+from sklearn.metrics import roc_auc_score, accuracy_score
+import copy
+import torch.nn.functional as F
+import random
+
+
+def train_deepmodel(device, model, loader, optimizer, criterion, epoch, model_name):
+    model.train()
+    running_loss = 0.0
+    corrects = 0.0
+    alpha = 1
+    beta = 0.1
+    for inputs, labels in tqdm(loader, desc=f'Training {model_name} Epoch {epoch + 1}', unit='batch'):
+        inputs, labels = inputs.float().to(device), labels.to(device)  # 确保数据在 device 上
+        optimizer.zero_grad()
+        
+        outputs, re_img = model(inputs)
+        loss = criterion(outputs.squeeze(), labels.float())
+        loss_F1 = F.l1_loss(re_img, inputs)
+        loss = alpha * loss + beta * loss_F1
+        loss.backward()
+        optimizer.step()
+        
+        running_loss += loss.item()
+    
+    avg_loss = running_loss / len(loader)
+    print(f'{model_name} Training Loss: {avg_loss:.4f}')
+    return avg_loss
+
+
+def validate_deepmodel(device, model, loader, criterion, epoch, model_name):
+    model.eval()
+    running_loss = 0.0
+    correct, total = 0, 0
+    all_labels, all_preds = [], []
+    val_corrects = 0.0
+    alpha = 1
+    beta = 0.1
+    
+    with torch.no_grad():
+        for inputs, labels in tqdm(loader, desc=f'Validating {model_name} Epoch {epoch + 1}', unit='batch'):
+            inputs, labels = inputs.float().to(device), labels.to(device)  # 确保数据在 device 上
+            
+            outputs, re_img = model(inputs)
+            
+            # 将 logits 转换为预测
+            predicted = torch.sigmoid(outputs).data
+            all_preds.extend(predicted.cpu().numpy())
+            all_labels.extend(labels.cpu().numpy())
+            
+            # loss = criterion(outputs.squeeze(), labels.float())
+            loss = criterion(outputs.squeeze(), labels.float())
+            loss_F1 = F.l1_loss(re_img, inputs)
+            loss = alpha * loss + beta * loss_F1
+            running_loss += loss.item()
+    
+    auc = roc_auc_score(all_labels, all_preds)
+    predicted_labels = (np.array(all_preds) >= 0.5).astype(int)  # 确保转换为 NumPy 数组
+    acc = accuracy_score(all_labels, predicted_labels)
+    avg_loss = running_loss / len(loader)
+    print(f'{model_name} Validation Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}, AUC: {auc:.4f}')
+    return avg_loss, acc, auc
+
+
+def test_deepmodel(device, model, loader):
+    model.eval()
+    all_labels, all_preds = [], []
+    
+    with torch.no_grad():
+        for inputs, labels in tqdm(loader, desc=f'Testing', unit='batch'):
+            inputs, labels = inputs.float().to(device), labels.to(device)  # 确保数据在 device 上
+            outputs, re_img = model(inputs)
+            predicted = torch.sigmoid(outputs).data  # 将 logits 转换为预测
+            
+            # 收集预测值和真实标签
+            all_preds.extend(predicted.cpu().numpy())
+            all_labels.extend(labels.cpu().numpy())
+    
+    # 将预测值转换为二值标签
+    predicted_labels = (np.array(all_preds) >= 0.5).astype(int)
+    
+    # 计算准确率和AUC
+    acc = accuracy_score(all_labels, predicted_labels)
+    auc = roc_auc_score(all_labels, all_preds)
+    
+    print(f'Test Accuracy: {acc:.4f}, Test AUC: {auc:.4f}')
+    return acc, auc
+
+
+# def train_model(device, model, loader, optimizer, criterion, epoch, model_name):
+#     model.train()
+#     running_loss = 0.0
+#     for i, (inputs, labels) in enumerate(tqdm(loader, desc=f'Training {model_name} Epoch {epoch + 1}', unit='batch')):
+#         inputs, labels = inputs.float().to(device), labels.float().to(device)  # 确保数据格式正确
+#         optimizer.zero_grad()
+#
+#         outputs = model(inputs)
+#         loss = criterion(outputs.squeeze(), labels)
+#
+#         # 随机打印部分输出和标签，检查格式
+#         if i % 10 == 0:  # 每100个批次打印一次
+#             print(f"Batch {i} - Sample Output: {outputs[0].item():.4f}, Sample Label: {labels[0].item()}")
+#
+#         # 检查损失值是否异常
+#         if loss.item() < 0:
+#             print(f"Warning: Negative loss detected at batch {i}. Loss: {loss.item()}")
+#
+#         loss.backward()
+#         optimizer.step()
+#
+#         running_loss += loss.item()
+#
+#     avg_loss = running_loss / len(loader)
+#     print(f'{model_name} Training Loss: {avg_loss:.4f}')
+#     return avg_loss
+def train_model(device, model, loader, optimizer, criterion, epoch, model_name):
+    model.train()
+    running_loss = 0.0
+    for inputs, labels in tqdm(loader, desc=f'Training {model_name} Epoch {epoch + 1}', unit='batch'):
+        inputs, labels = inputs.float().to(device), labels.to(device)  # 确保数据在 device 上
+        optimizer.zero_grad()
+        
+        outputs = model(inputs)
+        loss = criterion(outputs.squeeze(), labels.float())
+        loss.backward()
+        optimizer.step()
+        
+        running_loss += loss.item()
+    
+    avg_loss = running_loss / len(loader)
+    print(f'{model_name} Training Loss: {avg_loss:.4f}')
+    return avg_loss
+
+
+def validate_model(device, model, loader, criterion, epoch, model_name):
+    model.eval()
+    running_loss = 0.0
+    correct, total = 0, 0
+    all_labels, all_preds = [], []
+    
+    with torch.no_grad():
+        for inputs, labels in tqdm(loader, desc=f'Validating {model_name} Epoch {epoch + 1}', unit='batch'):
+            inputs, labels = inputs.float().to(device), labels.to(device)  # 确保数据在 device 上
+            
+            outputs = model(inputs)
+            
+            # 将 logits 转换为预测
+            predicted = torch.sigmoid(outputs).data
+            all_preds.extend(predicted.cpu().numpy())
+            all_labels.extend(labels.cpu().numpy())
+            
+            # loss = criterion(outputs.squeeze(), labels.float())
+            loss = criterion(outputs.squeeze(), labels.float())
+            running_loss += loss.item()
+    auc = roc_auc_score(all_labels, all_preds)
+    predicted_labels = (np.array(all_preds) >= 0.5).astype(int)  # 确保转换为 NumPy 数组
+    acc = accuracy_score(all_labels, predicted_labels)
+    avg_loss = running_loss / len(loader)
+    print(f'{model_name} Validation Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}, AUC: {auc:.4f}')
+    return avg_loss, acc, auc
+
+
+# 权重聚合函数
+def aggregate_weights(weights_list, alpha=1 / 3, beta=1 / 3, gamma=1 / 3):
+    new_state_dict = copy.deepcopy(weights_list[0])  # 从模型a复制权重结构
+    for key in new_state_dict.keys():
+        new_state_dict[key] = (alpha * weights_list[0][key] +
+                               beta * weights_list[1][key] +
+                               gamma * weights_list[2][key])
+    return new_state_dict
+
+
+def v3_update_model_weights(
+        epoch,
+        model_to_update,
+        other_models,
+        global_model,
+        losses,
+        val_loader,
+        device,
+        val_auc_threshold,  # 当前需要更新模型的验证 AUC 阈值
+        validate_model,
+        criterion,
+        update_frequency
+):
+    """
+    根据给定的条件更新模型的权重。
+
+    参数:
+        epoch (int): 当前训练轮次。
+        model_to_update: 需要更新的模型。
+        other_models (list): 其他模型列表，用于计算全局模型权重。
+        global_model: 全局模型。
+        losses (list): 各模型的损失值列表。
+        val_loader: 验证数据的 DataLoader。
+        device: 设备 ('cuda' 或 'cpu')。
+        val_auc_threshold (float): 当前需要更新模型的验证 AUC。
+        aggregate_weights (function): 权重聚合函数。
+        validate_model (function): 验证模型的函数。
+        update_frequency (int): 权重更新的频率。
+
+    返回:
+        val_acc (float): 全局模型的验证精度。
+        val_auc (float): 全局模型的验证 AUC。
+        updated_val_auc_threshold (float): 更新后的验证 AUC。
+    """
+    if (epoch + 1) % update_frequency == 0:
+        # 获取所有模型的权重
+        all_weights = [model.state_dict() for model in other_models]
+        avg_weights = aggregate_weights(all_weights)  # 聚合权重
+        
+        # 更新全局模型权重
+        global_model.load_state_dict(avg_weights)
+        
+        # 计算加权平均损失
+        weighted_loss = sum(loss * 0.33 for loss in losses)
+        print(f"Weighted Average Loss: {weighted_loss:.4f}")
+        
+        # 验证全局模型
+        val_loss, val_acc, val_auc = validate_model(device, global_model, val_loader, criterion, epoch, 'global_model')
+        print(f'global_model Validation Accuracy: {val_acc:.4f}, global_model Validation AUC: {val_auc:.4f}')
+        
+        # 如果全局模型的 AUC 更高，则更新目标模型
+        if val_auc > val_auc_threshold:
+            print(f'Updating model at epoch {epoch + 1}')
+            model_to_update.load_state_dict(global_model.state_dict())
+            val_auc_threshold = val_auc  # 更新 AUC 阈值
+        
+        return val_acc, val_auc, val_auc_threshold
+    return None, None, val_auc_threshold
+
+
+def update_model_weights(
+        epoch,
+        model_to_update,
+        other_models,
+        global_model,
+        losses,
+        val_loader,
+        device,
+        val_auc_threshold,  # 当前需要更新模型的验证 AUC 阈值
+        validate_model,
+        criterion,
+        update_frequency
+):
+    """
+    根据给定的条件更新模型的权重。
+
+    参数:
+        epoch (int): 当前训练轮次。
+        model_to_update: 需要更新的模型。
+        other_models (list): 其他模型列表，用于计算全局模型权重。
+        global_model: 全局模型。
+        losses (list): 各模型的损失值列表。
+        val_loader: 验证数据的 DataLoader。
+        device: 设备 ('cuda' 或 'cpu')。
+        val_auc_threshold (float): 当前需要更新模型的验证 AUC。
+        aggregate_weights (function): 权重聚合函数。
+        validate_model (function): 验证模型的函数。
+        update_frequency (int): 权重更新的频率。
+
+    返回:
+        val_acc (float): 全局模型的验证精度。
+        val_auc (float): 全局模型的验证 AUC。
+        updated_val_auc_threshold (float): 更新后的验证 AUC。
+    """
+    if (epoch + 1) % update_frequency == 0:
+        # 获取所有模型的权重
+        all_weights = [model.state_dict() for model in other_models]
+        avg_weights = aggregate_weights(all_weights)  # 聚合权重
+        
+        # 更新全局模型权重
+        global_model.load_state_dict(avg_weights)
+        
+        # 计算加权平均损失
+        weighted_loss = sum(loss * 0.33 for loss in losses)
+        print(f"Weighted Average Loss: {weighted_loss:.4f}")
+        
+        # 验证全局模型
+        val_loss, val_acc, val_auc = validate_deepmodel(device, global_model, val_loader, criterion, epoch,
+                                                        'global_model')
+        print(f'global_model Validation Accuracy: {val_acc:.4f}, global_model Validation AUC: {val_auc:.4f}')
+        
+        # 如果全局模型的 AUC 更高，则更新目标模型
+        if val_auc > val_auc_threshold:
+            print(f'Updating model at epoch {epoch + 1}')
+            model_to_update.load_state_dict(global_model.state_dict())
+            val_auc_threshold = val_auc  # 更新 AUC 阈值
+        
+        return val_acc, val_auc, val_auc_threshold
+    return None, None, val_auc_threshold
+
+
+def f_update_model_weights(
+        epoch,
+        model_to_update,
+        other_models,
+        global_model,
+        losses,
+        val_loader,
+        device,
+        val_auc_threshold,  # 当前需要更新模型的验证 AUC 阈值
+        aggregate_weights,  # 权重聚合函数
+        validate_model,
+        criterion,
+        update_frequency
+):
+    """
+    根据给定的条件更新模型的权重。
+
+    参数:
+        epoch (int): 当前训练轮次。
+        model_to_update: 需要更新的模型。
+        other_models (list): 其他模型列表，用于计算全局模型权重。
+        global_model: 全局模型。
+        losses (list): 各模型的损失值列表。
+        val_loader: 验证数据的 DataLoader。
+        device: 设备 ('cuda' 或 'cpu')。
+        val_auc_threshold (float): 当前需要更新模型的验证 AUC 阈值。
+        aggregate_weights (function): 权重聚合函数。
+        validate_model (function): 验证模型的函数。
+        criterion: 损失函数。
+        update_frequency (int): 权重更新的频率。
+
+    返回:
+        val_acc (float): 全局模型的验证精度。
+        val_auc (float): 全局模型的验证 AUC。
+        updated_val_auc_threshold (float): 更新后的验证 AUC 阈值。
+    """
+    # 每隔指定的 epoch 更新一次模型权重
+    if (epoch + 1) % update_frequency == 0:
+        print(f"\n[Epoch {epoch + 1}] Updating global model weights...")
+        
+        # 获取其他模型的权重
+        all_weights = [model.state_dict() for model in other_models]
+        
+        # 使用聚合函数计算全局权重
+        avg_weights = aggregate_weights(all_weights)
+        print("Global model weights aggregated.")
+        
+        # 更新全局模型权重
+        global_model.load_state_dict(avg_weights)
+        
+        # 计算加权平均损失
+        weighted_loss = sum(loss * (1 / len(losses)) for loss in losses)  # 平均加权
+        print(f"Weighted Average Loss: {weighted_loss:.4f}")
+        
+        # 验证全局模型性能
+        val_loss, val_acc, val_auc = validate_deepmodel(device, global_model, val_loader, criterion, epoch,
+                                                        'global_model')
+        print(f"[Global Model] Validation Loss: {val_loss:.4f}, Accuracy: {val_acc:.4f}, AUC: {val_auc:.4f}")
+        
+        # 如果全局模型 AUC 高于阈值，则更新目标模型权重
+        if val_auc > val_auc_threshold:
+            print(f"Global model AUC improved ({val_auc:.4f} > {val_auc_threshold:.4f}). Updating target model.")
+            model_to_update.load_state_dict(global_model.state_dict())
+            val_auc_threshold = val_auc  # 更新 AUC 阈值
+        else:
+            print(
+                f"Global model AUC did not improve ({val_auc:.4f} <= {val_auc_threshold:.4f}). No update to target model.")
+        
+        return val_acc, val_auc, val_auc_threshold
+    
+    # 如果未到达更新频率，返回当前的 AUC 阈值
+    return None, None, val_auc_threshold