60_隐私保护模型：联邦学习变体

模型规模大 → 参数数量多 → 隐私保护成本高
上下文长度长 → 处理数据量大 → 噪声注入难度增加
生成能力强 → 逆向推导风险高 → 防护机制复杂

初始化 → 本地训练 → 参数上传 → 全局聚合 → 参数分发 → 迭代优化

# FedAvg 2025版本实现示例
import torch
import numpy as np
from typing import List, Dict, Any

class FedAvgAggregator:
    def __init__(self, model, clients_weight=None):
        self.global_model = model
        self.clients_weight = clients_weight
    
    def aggregate(self, client_models: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
        # 初始化聚合参数
        aggregated_params = {}
        client_count = len(client_models)
        
        # 计算权重（如果未提供）
        if self.clients_weight is None:
            weights = [1.0 / client_count] * client_count
        else:
            weights = self.clients_weight
        
        # 对每个参数进行加权平均
        for param_name in self.global_model.state_dict():
            # 确保所有客户端都有该参数
            if all(param_name in client_model for client_model in client_models):
                # 初始化聚合参数
                aggregated_params[param_name] = torch.zeros_like(
                    self.global_model.state_dict()[param_name]
                )
                
                # 加权平均
                for i, client_model in enumerate(client_models):
                    aggregated_params[param_name] += weights[i] * client_model[param_name]
        
        return aggregated_params

服务器层 → 区域聚合层 → 客户端层

Pr[M(D) ∈ S] ≤ e^ε × Pr[M(D') ∈ S]

# LLM训练中的差分隐私噪声注入示例
import torch
import numpy as np

class DPPrivacyEngine:
    def __init__(self, epsilon, delta, max_grad_norm, noise_multiplier):
        self.epsilon = epsilon
        self.delta = delta
        self.max_grad_norm = max_grad_norm
        self.noise_multiplier = noise_multiplier
    
    def clip_and_noise_gradients(self, parameters, batch_size):
        # 梯度裁剪
        total_norm = 0
        for p in parameters:
            if p.grad is not None:
                param_norm = p.grad.data.norm(2)
                total_norm += param_norm.item() ** 2
        total_norm = total_norm ** 0.5
        
        clip_coef = self.max_grad_norm / (total_norm + 1e-6)
        if clip_coef < 1:
            for p in parameters:
                if p.grad is not None:
                    p.grad.data.mul_(clip_coef)
        
        # 噪声注入
        noise_scale = self.noise_multiplier * self.max_grad_norm / batch_size
        for p in parameters:
            if p.grad is not None:
                noise = torch.randn_like(p.grad) * noise_scale
                p.grad.data.add_(noise)
        
        return parameters

# 隐私预算动态分配示例
class PrivacyBudgetScheduler:
    def __init__(self, total_epsilon, total_epochs, strategy="exponential"):
        self.total_epsilon = total_epsilon
        self.total_epochs = total_epochs
        self.strategy = strategy
    
    def get_epoch_epsilon(self, epoch):
        if self.strategy == "linear":
            # 线性分配
            return self.total_epsilon / self.total_epochs
        elif self.strategy == "exponential":
            # 指数分配，前期使用更多预算
            exponent = epoch / (self.total_epochs - 1)
            weight = np.exp(-5 * (1 - exponent))  # 指数衰减
            return weight * self.total_epsilon
        elif self.strategy == "warmup":
            # 预热策略，前半部分逐渐增加，后半部分保持
            if epoch < self.total_epochs / 2:
                return (epoch / (self.total_epochs / 2)) * (2 * self.total_epsilon / self.total_epochs)
            else:
                return 2 * self.total_epsilon / self.total_epochs
        else:
            raise ValueError(f"Unknown strategy: {self.strategy}")

# 简化的安全聚合协议示例
class SecureAggregator:
    def __init__(self, threshold=3):
        self.threshold = threshold  # 阈值秘密共享参数
    
    def setup(self, clients):
        # 初始化安全参数
        self.clients = clients
        self.n_clients = len(clients)
        # 生成秘密共享参数
        self.generate_sharing_params()
    
    def generate_sharing_params(self):
        # 生成多项式系数等参数
        # 实际实现需要复杂的密码学操作
        pass
    
    async def secure_aggregate(self, client_updates):
        # 1. 每个客户端对其更新进行秘密共享
        shares = []
        for client_id, update in enumerate(client_updates):
            client_shares = self.share_update(update, client_id)
            shares.append(client_shares)
        
        # 2. 客户端之间交换共享分片
        await self.exchange_shares(shares)
        
        # 3. 每个客户端计算其收到的分片之和
        partial_sums = await self.compute_partial_sums()
        
        # 4. 重建聚合结果
        aggregated_update = self.reconstruct_aggregate(partial_sums)
        
        return aggregated_update
    
    def share_update(self, update, client_id):
        # 实现秘密共享逻辑
        # 返回分配给每个客户端的分片
        pass

# 同态加密LLM推理简化示例
from tenseal import Context, EncryptionParameters, SchemeType
import numpy as np

class HomomorphicLLMInference:
    def __init__(self):
        # 设置同态加密参数
        self.setup_encryption()
    
    def setup_encryption(self):
        # 创建加密参数
        parms = EncryptionParameters(SchemeType.CKKS)
        parms.set_poly_modulus_degree(8192)
        parms.set_coeff_modulus([60, 40, 40, 60])
        
        # 创建上下文
        self.context = Context(parms)
        self.context.global_scale = 2**40
        
        # 生成密钥对
        self.public_key = self.context.generate_public_key()
        self.secret_key = self.context.secret_key()
    
    def encrypt_input(self, input_text):
        # 文本向量化（实际实现需要更复杂的处理）
        input_vector = self.text_to_vector(input_text)
        
        # 加密输入向量
        encrypted_input = self.context.encrypt(input_vector)
        return encrypted_input
    
    def text_to_vector(self, text):
        # 简化的文本向量化，实际实现需要使用分词器和词嵌入
        # 这里仅作为示例
        return np.array([0.1, 0.2, 0.3, 0.4])
    
    def homomorphic_inference(self, encrypted_input, model_weights):
        # 在密文上执行前向传播（简化版）
        # 实际LLM推理需要实现注意力机制等复杂操作
        encrypted_output = encrypted_input.dot(model_weights)
        return encrypted_output
    
    def decrypt_output(self, encrypted_output):
        # 解密结果
        decrypted_output = encrypted_output.decrypt(self.secret_key)
        return decrypted_output

客户端 → 输入加密 → 加密数据传输 → 云端同态推理 → 加密结果传输 → 客户端解密

# 联邦学习通信优化示例
class CommunicationOptimizer:
    def __init__(self, compression_ratio=0.1):
        self.compression_ratio = compression_ratio
    
    def sparse_pruning(self, params):
        # 稀疏化参数，只传输重要参数
        compressed_params = {}
        for name, param in params.items():
            # 使用Top-k稀疏化
            k = max(1, int(self.compression_ratio * param.numel()))
            values, indices = torch.topk(param.abs().flatten(), k)
            mask = torch.zeros_like(param.flatten())
            mask[indices] = 1
            mask = mask.reshape(param.shape)
            compressed_params[name] = {
                'values': param[mask.bool()],
                'indices': indices,
                'shape': param.shape
            }
        return compressed_params
    
    def reconstruct_params(self, compressed_params):
        # 重构参数
        params = {}
        for name, data in compressed_params.items():
            param = torch.zeros(data['shape'])
            param.view(-1)[data['indices']] = data['values']
            params[name] = param
        return params
    
    def quantization(self, params, bits=8):
        # 参数量化
        compressed_params = {}
        for name, param in params.items():
            # 线性量化
            min_val = param.min()
            max_val = param.max()
            scale = (max_val - min_val) / (2**bits - 1)
            quantized = ((param - min_val) / scale).round().byte()
            compressed_params[name] = {
                'data': quantized,
                'min_val': min_val,
                'max_val': max_val,
                'bits': bits
            }
        return compressed_params

集中式预训练 → 联邦微调 → 个性化适应

# 成员推断攻击评估示例
class MembershipInferenceEvaluator:
    def __init__(self, model, train_data, test_data):
        self.model = model
        self.train_data = train_data
        self.test_data = test_data
    
    def evaluate(self, num_samples=1000):
        # 从训练集和测试集中随机选择样本
        train_samples = random.sample(self.train_data, min(num_samples, len(self.train_data)))
        test_samples = random.sample(self.test_data, min(num_samples, len(self.test_data)))
        
        # 计算模型对样本的置信度
        train_confidences = self.get_confidences(train_samples)
        test_confidences = self.get_confidences(test_samples)
        
        # 构建攻击模型（简单阈值方法）
        # 实际攻击可能更复杂
        threshold = self.find_optimal_threshold(train_confidences, test_confidences)
        
        # 评估攻击准确率
        train_preds = [conf > threshold for conf in train_confidences]
        test_preds = [conf > threshold for conf in test_confidences]
        
        true_positives = sum(train_preds)
        true_negatives = len(test_preds) - sum(test_preds)
        accuracy = (true_positives + true_negatives) / (len(train_preds) + len(test_preds))
        
        return {
            'attack_accuracy': accuracy,
            'privacy_risk': accuracy - 0.5,  # 基线是随机猜测（50%准确率）
            'threshold': threshold
        }
    
    def get_confidences(self, samples):
        # 获取模型对样本的置信度
        confidences = []
        self.model.eval()
        with torch.no_grad():
            for sample in samples:
                # 简化示例，实际需要根据模型类型调整
                outputs = self.model(**sample)
                confidence = outputs.logits.softmax(dim=-1).max().item()
                confidences.append(confidence)
        return confidences
    
    def find_optimal_threshold(self, train_conf, test_conf):
        # 找到最优阈值
        # 简化实现，实际可能使用更复杂的方法
        all_conf = sorted(train_conf + test_conf)
        best_acc = 0
        best_threshold = 0
        
        for threshold in all_conf:
            train_preds = [c > threshold for c in train_conf]
            test_preds = [c > threshold for c in test_conf]
            acc = (sum(train_preds) + (len(test_preds) - sum(test_preds))) / (len(train_preds) + len(test_preds))
            if acc > best_acc:
                best_acc = acc
                best_threshold = threshold
        
        return best_threshold

# 医疗领域联邦学习示例
class MedicalFederatedLearning:
    def __init__(self, hospitals, central_server):
        self.hospitals = hospitals
        self.central_server = central_server
    
    async def train_medical_llm(self, num_rounds=10):
        # 初始化全局模型
        global_model = self.central_server.initialize_model()
        
        for round_num in range(num_rounds):
            print(f"Round {round_num+1}/{num_rounds}")
            
            # 选择参与本轮训练的医院
            participating_hospitals = self.select_participants()
            
            # 分发当前全局模型
            await self.distribute_model(global_model, participating_hospitals)
            
            # 医院本地训练（使用差分隐私）
            local_models = await asyncio.gather(
                *(hospital.train_locally(epsilon=0.1, delta=1e-5) 
                  for hospital in participating_hospitals)
            )
            
            # 安全聚合本地模型（使用安全多方计算）
            aggregated_model = await self.secure_aggregate(local_models)
            
            # 更新全局模型
            global_model = aggregated_model
            
            # 评估模型性能
            performance = await self.evaluate_model(global_model)
            print(f"Round {round_num+1} performance: {performance}")
        
        return global_model
    
    def select_participants(self):
        # 选择参与训练的医院
        # 实际实现可能基于活跃度、数据质量等因素
        return self.hospitals
    
    async def distribute_model(self, model, hospitals):
        # 分发模型到各医院
        tasks = [hospital.receive_model(model) for hospital in hospitals]
        await asyncio.gather(*tasks)
    
    async def secure_aggregate(self, local_models):
        # 使用安全多方计算进行模型聚合
        # 实际实现需要复杂的密码学协议
        pass
    
    async def evaluate_model(self, model):
        # 在保护隐私的测试集上评估模型
        pass