Federated Learning Dqn

Federated Learning + DQN

Privacy-preserving distributed reinforcement learning for healthcare scheduling.

When to Use

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

Architecture Overview

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

Components

Federated Learning

FedAvg Algorithm:

# 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))

Deep Q-Network (DQN)

Network Architecture:

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:

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:

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

Differential Privacy

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

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

Healthcare Scheduling Use Case

State Representation

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

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

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

Communication Efficiency

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

Handling Non-IID Data

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

System Heterogeneity

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

Evaluation Metrics

| 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 |

Resources

• Federated Learning Paper (McMahan et al.)
• DQN Paper (Mnih et al.)
• Healthcare Scheduling Survey

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kinhluan
• Source: kinhluan/skills
• License: MIT

Install and usage instructions live in the source repository linked above.