federated-learning-dqn

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Chinese

Federated Learning + DQN

Privacy-preserving distributed reinforcement learning for healthcare scheduling.

面向医疗调度的隐私保护分布式强化学习方案。

When to Use

适用场景

Multi-institution ML without sharing raw data
Healthcare applications with privacy requirements
Distributed optimization across organizations

多机构机器学习，无需共享原始数据
有隐私要求的医疗应用
跨组织的分布式优化

Architecture Overview

架构概述

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Hospital A │     │  Hospital B │     │  Hospital C │
│  Local DQN  │     │  Local DQN  │     │  Local DQN  │
└──────┬──────┘     └──────┬──────┘     └──────┬──────┘
       │                   │                   │
       └───────────────────┼───────────────────┘
                           │
                    ┌──────▼──────┐
                    │  Aggregator │
                    │  (Server)   │
                    └─────────────┘

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Hospital A │     │  Hospital B │     │  Hospital C │
│  Local DQN  │     │  Local DQN  │     │  Local DQN  │
└──────┬──────┘     └──────┬──────┘     └──────┬──────┘
       │                   │                   │
       └───────────────────┼───────────────────┘
                           │
                    ┌──────▼──────┐
                    │  Aggregator │
                    │  (Server)   │
                    └─────────────┘

Components

组件

Federated Learning

FedAvg Algorithm:

python

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FedAvg Algorithm:

python

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Server

def federated_averaging(models, weights): total = sum(weights) averaged = {} for key in models[0].state_dict(): averaged[key] = sum( w * model.state_dict()[key] for model, w in zip(models, weights) ) / total return averaged

Round

for round in range(num_rounds): clients = select_clients() models, weights = [], [] for client in clients: model, weight = client.train(local_epochs) models.append(model) weights.append(weight) global_model.load_state_dict(federated_averaging(models, weights))

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Deep Q-Network (DQN)

Network Architecture:

python

import torch.nn as nn

class DQN(nn.Module):
    def __init__(self, state_dim, action_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(state_dim, 256),
            nn.ReLU(),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Linear(256, action_dim)
        )
    
    def forward(self, x):
        return self.net(x)

Training Loop:

python

def train_dqn(agent, replay_buffer, target_net):
    for step in range(num_steps):
        state = env.reset()
        done = False
        
        while not done:
            # Epsilon-greedy action
            action = agent.select_action(state, epsilon)
            next_state, reward, done, _ = env.step(action)
            
            # Store transition
            replay_buffer.push(state, action, reward, next_state, done)
            
            # Sample batch
            batch = replay_buffer.sample(batch_size)
            
            # Compute loss
            q_values = agent(batch.state)
            next_q_values = target_net(batch.next_state)
            target = batch.reward + gamma * next_q_values.max(1)[0] * (1 - batch.done)
            loss = nn.MSELoss()(q_values.gather(1, batch.action), target)
            
            # Update
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            state = next_state
        
        # Update target network
        if step % target_update == 0:
            target_net.load_state_dict(agent.state_dict())

Network Architecture:

python

import torch.nn as nn

class DQN(nn.Module):
    def __init__(self, state_dim, action_dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(state_dim, 256),
            nn.ReLU(),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Linear(256, action_dim)
        )
    
    def forward(self, x):
        return self.net(x)

训练循环:

python

def train_dqn(agent, replay_buffer, target_net):
    for step in range(num_steps):
        state = env.reset()
        done = False
        
        while not done:
            # Epsilon-greedy action
            action = agent.select_action(state, epsilon)
            next_state, reward, done, _ = env.step(action)
            
            # Store transition
            replay_buffer.push(state, action, reward, next_state, done)
            
            # Sample batch
            batch = replay_buffer.sample(batch_size)
            
            # Compute loss
            q_values = agent(batch.state)
            next_q_values = target_net(batch.next_state)
            target = batch.reward + gamma * next_q_values.max(1)[0] * (1 - batch.done)
            loss = nn.MSELoss()(q_values.gather(1, batch.action), target)
            
            # Update
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            state = next_state
        
        # Update target network
        if step % target_update == 0:
            target_net.load_state_dict(agent.state_dict())

Multi-Level Feedback Queue (MLFQ)

Integration with DQN:

python

class MLFQScheduler:
    def __init__(self, num_queues=3):
        self.queues = [[] for _ in range(num_queues)]
        self.priority_boost = 10
        
    def add_patient(self, patient, priority):
        queue_idx = min(priority, len(self.queues) - 1)
        self.queues[queue_idx].append(patient)
    
    def get_next_patient(self):
        # DQN selects which queue to serve
        queue_state = self.get_queue_state()
        action = dqn_agent.select_action(queue_state)
        
        # Boost priority of waiting patients
        self.boost_priorities()
        
        return self.queues[action].pop(0) if self.queues[action] else None
    
    def boost_priorities(self):
        for i in range(len(self.queues) - 1, 0, -1):
            for patient in self.queues[i]:
                if patient.wait_time > self.priority_boost:
                    self.queues[i-1].append(patient)
                    self.queues[i].remove(patient)

与DQN的集成:

python

class MLFQScheduler:
    def __init__(self, num_queues=3):
        self.queues = [[] for _ in range(num_queues)]
        self.priority_boost = 10
        
    def add_patient(self, patient, priority):
        queue_idx = min(priority, len(self.queues) - 1)
        self.queues[queue_idx].append(patient)
    
    def get_next_patient(self):
        # DQN selects which queue to serve
        queue_state = self.get_queue_state()
        action = dqn_agent.select_action(queue_state)
        
        # Boost priority of waiting patients
        self.boost_priorities()
        
        return self.queues[action].pop(0) if self.queues[action] else None
    
    def boost_priorities(self):
        for i in range(len(self.queues) - 1, 0, -1):
            for patient in self.queues[i]:
                if patient.wait_time > self.priority_boost:
                    self.queues[i-1].append(patient)
                    self.queues[i].remove(patient)

Privacy Guarantees

隐私保障

Differential Privacy

python

def add_dp_noise(gradients, epsilon, delta, sensitivity):
    """Add Gaussian noise for (ε,δ)-differential privacy"""
    sigma = sensitivity * np.sqrt(2 * np.log(1.25 / delta)) / epsilon
    noise = torch.randn_like(gradients) * sigma
    return gradients + noise

python

def add_dp_noise(gradients, epsilon, delta, sensitivity):
    """Add Gaussian noise for (ε,δ)-differential privacy"""
    sigma = sensitivity * np.sqrt(2 * np.log(1.25 / delta)) / epsilon
    noise = torch.randn_like(gradients) * sigma
    return gradients + noise

Secure Aggregation

安全聚合

Clients encrypt model updates
Server aggregates without seeing individual updates
Only decrypted aggregate is visible

客户端加密模型更新
服务器在不查看单个更新的情况下进行聚合
仅可见解密后的聚合结果

Healthcare Scheduling Use Case

医疗调度用例

State Representation

状态表示

python

state = {
    'queue_lengths': [len(q) for q in queues],  # Shape: (num_queues,)
    'patient_acuity': average_acuity_per_queue,  # Shape: (num_queues,)
    'resource_availability': [beds, staff, equipment],
    'time_features': [hour_of_day, day_of_week],
    'predicted_arrivals': next_hour_forecast,
}

python

state = {
    'queue_lengths': [len(q) for q in queues],  # Shape: (num_queues,)
    'patient_acuity': average_acuity_per_queue,  # Shape: (num_queues,)
    'resource_availability': [beds, staff, equipment],
    'time_features': [hour_of_day, day_of_week],
    'predicted_arrivals': next_hour_forecast,
}

Action Space

动作空间

python

actions = {
    0: 'Schedule from high-priority queue',
    1: 'Schedule from medium-priority queue',
    2: 'Schedule from low-priority queue',
    3: 'Allocate additional resource',
    4: 'Request transfer from other hospital',
}

python

actions = {
    0: 'Schedule from high-priority queue',
    1: 'Schedule from medium-priority queue',
    2: 'Schedule from low-priority queue',
    3: 'Allocate additional resource',
    4: 'Request transfer from other hospital',
}

Reward Function

奖励函数

python

def calculate_reward(state, action, next_state):
    reward = 0
    
    # Minimize wait time (weighted by acuity)
    reward -= sum(
        patient.wait_time * patient.acuity 
        for patient in all_patients
    )
    
    # Penalize queue imbalance
    reward -= variance(queue_lengths) * 10
    
    # Reward completing high-acuity cases
    reward += completed_high_acuity * 50
    
    # Penalize resource overutilization
    if resource_utilization > threshold:
        reward -= overutilization_penalty
    
    return reward

python

def calculate_reward(state, action, next_state):
    reward = 0
    
    # Minimize wait time (weighted by acuity)
    reward -= sum(
        patient.wait_time * patient.acuity 
        for patient in all_patients
    )
    
    # Penalize queue imbalance
    reward -= variance(queue_lengths) * 10
    
    # Reward completing high-acuity cases
    reward += completed_high_acuity * 50
    
    # Penalize resource overutilization
    if resource_utilization > threshold:
        reward -= overutilization_penalty
    
    return reward

Implementation Considerations

实现注意事项

Communication Efficiency

通信效率

Compression: Quantize model updates
Federated Dropout: Train smaller subnetworks
Asynchronous Updates: No synchronization barrier

压缩: 量化模型更新
Federated Dropout: 训练更小的子网络
异步更新: 无需同步屏障

Handling Non-IID Data

非IID数据处理

Personalization: Fine-tune global model locally
Clustered FL: Group similar hospitals
Multi-task Learning: Shared representation + task-specific heads

个性化: 在本地微调全局模型
Clustered FL: 将相似医院分组
多任务学习: 共享表示+任务特定头部

System Heterogeneity

系统异构性

Straggler Handling: Async aggregation or timeout
Variable Resources: Adaptive local epochs
Device Selection: Probabilistic client sampling

掉队节点处理: 异步聚合或超时机制
资源可变: 自适应本地训练轮次
设备选择: 概率性客户端采样

Evaluation Metrics

评估指标

Metric	Description
Privacy Budget (ε)	Differential privacy guarantee
Model Accuracy	Comparison to centralized training
Communication Rounds	Convergence speed
Patient Wait Time	Scheduling effectiveness
Resource Utilization	System efficiency

指标	描述
隐私预算(ε)	差分隐私保障
模型准确率	与集中式训练的对比
通信轮次	收敛速度
患者等待时间	调度有效性
资源利用率	系统效率

federated-learning-dqn

Original

Translation

Federated Learning + DQN

Federated Learning + DQN

When to Use

适用场景

Architecture Overview

架构概述

Components

组件

Federated Learning

Federated Learning

Server

Server

Round

Round

Deep Q-Network (DQN)

Deep Q-Network (DQN)

Multi-Level Feedback Queue (MLFQ)

Multi-Level Feedback Queue (MLFQ)

Privacy Guarantees

隐私保障

Differential Privacy

Differential Privacy

Secure Aggregation

安全聚合

Healthcare Scheduling Use Case

医疗调度用例

State Representation

状态表示

Action Space

动作空间

Reward Function

奖励函数

Implementation Considerations

实现注意事项

Communication Efficiency

通信效率

Handling Non-IID Data

非IID数据处理

System Heterogeneity

系统异构性

Evaluation Metrics

评估指标

Resources

参考资源