Federated Learning: Move the Model, Not the Data

Federated learning enables AI model training without centralized datasets. Through distributed collaborative learning, it achieves cross-organization and cross-region intelligent collaboration while protecting data privacy.

Core Technologies of Federated Learning

🔐 Privacy Protection Mechanisms

  • • Differential privacy
  • • Homomorphic encryption
  • • Secure multi-party computation
  • • Trusted execution environments

🌐 Distributed Architectures

  • • Horizontal federated learning
  • • Vertical federated learning
  • • Federated transfer learning
  • • Decentralized federation

⚡ Optimization Algorithms

  • • FedAvg algorithm
  • • FedProx optimization
  • • Adaptive aggregation
  • • Asynchronous updates

🛡️ Security Protection

  • • Poisoning attack defense
  • • Model theft protection
  • • Gradient leakage defense
  • • Byzantine fault tolerance

How Federated Learning Works

Distributed Collaborative Training Flow

class FederatedLearningServer:
    """Federated learning server"""
    
    def __init__(self, model, num_clients, privacy_budget):
        self.global_model = model
        self.num_clients = num_clients
        self.privacy_budget = privacy_budget
        self.round = 0
        
    def federated_averaging(self, client_updates):
        """Federated averaging algorithm"""
        # 1. Aggregate client updates
        aggregated_weights = {}
        total_samples = sum(update['num_samples'] for update in client_updates)
        
        for layer_name in self.global_model.state_dict():
            # Weighted average
            weighted_sum = torch.zeros_like(
                self.global_model.state_dict()[layer_name]
            )
            
            for update in client_updates:
                weight = update['num_samples'] / total_samples
                weighted_sum += weight * update['weights'][layer_name]
            
            # Add differential privacy noise
            if self.privacy_budget > 0:
                noise = self.add_differential_privacy(
                    weighted_sum, 
                    self.privacy_budget
                )
                weighted_sum += noise
            
            aggregated_weights[layer_name] = weighted_sum
        
        # 2. Update global model
        self.global_model.load_state_dict(aggregated_weights)
        self.round += 1
        
        return self.global_model
    
    def secure_aggregation(self, encrypted_gradients):
        """Secure aggregation protocol"""
        # Use homomorphic encryption for secure aggregation
        aggregated = HomomorphicSum(encrypted_gradients)
        
        # Decrypt only after threshold is met
        if len(encrypted_gradients) >= self.threshold:
            decrypted = self.decrypt_aggregated(aggregated)
            return decrypted
        
        return None

class FederatedClient:
    """Federated learning client"""
    
    def __init__(self, client_id, local_data, model):
        self.client_id = client_id
        self.local_data = local_data
        self.model = copy.deepcopy(model)
        
    def local_training(self, epochs=5):
        """Local training"""
        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)
        
        for epoch in range(epochs):
            for batch in self.local_data:
                # Forward pass
                loss = self.model.compute_loss(batch)
                
                # Backward pass
                optimizer.zero_grad()
                loss.backward()
                
                # Gradient clipping (privacy protection)
                torch.nn.utils.clip_grad_norm_(
                    self.model.parameters(), 
                    max_norm=1.0
                )
                
                optimizer.step()
        
        # Compute update delta
        model_update = self.compute_update_diff()
        
        # Encrypt update (optional)
        encrypted_update = self.encrypt_update(model_update)
        
        return {
            'client_id': self.client_id,
            'weights': encrypted_update,
            'num_samples': len(self.local_data)
        }

Federated Learning Flowchart

1

Model distribution

The server sends the global model to selected clients

2

Local training

Clients train the model on local data

3

Upload updates

Encrypted model updates are sent back to the server

4

Aggregate updates

The server aggregates all updates to produce a new global model

Privacy Protection Technologies

Multi-layer Privacy Protection System

🔒 Differential Privacy

# Add Laplace noise
def add_laplace_noise(value, sensitivity, epsilon):
    scale = sensitivity / epsilon
    noise = np.random.laplace(0, scale)
    return value + noise
  • • Privacy budget management
  • • Noise calibration
  • • Privacy amplification

🔐 Homomorphic Encryption

# Homomorphic encrypted computation
encrypted_sum = HE.add(
    encrypted_grad1,
    encrypted_grad2
)
# Compute without decryption
  • • Computation on ciphertext
  • • End-to-end encryption
  • • Zero-knowledge proofs

Use Case Examples

Healthcare Data Federation

Scenario

Multiple hospitals collaboratively train disease prediction models. Patient data never leaves the hospital, protecting privacy.

Outcomes

  • • Model accuracy improved by 35%
  • • Case coverage increased 10x
  • • 100% data privacy protection

Financial Risk Control Alliance

Scenario

Banks share risk control model capabilities without sharing customer data.

Outcomes

  • • Fraud detection rate improved by 45%
  • • Risk alerts advanced by 2 days
  • • 100% compliance

Smartphone Keyboard

Scenario

Personalized input method model training with user data staying on-device.

Outcomes

  • • Input accuracy improved by 28%
  • • 100% on-device processing
  • • Zero privacy leakage risk

Technical Challenges and Solutions

Challenges in Federated Learning

⚡ Communication Efficiency

Challenge:

Frequent communication leads to high bandwidth usage

Solution:

Gradient compression, sparse updates, multiple local epochs

🌐 Heterogeneity Handling

Challenge:

Non-IID client data distribution and varying compute capacity

Solution:

Personalized federated learning and adaptive aggregation strategies

🛡️ Security Threats

Challenge:

Malicious clients and model theft

Solution:

Robust aggregation algorithms and anomaly detection mechanisms

Implementation Guide

Federated Learning Deployment Steps

1️⃣ Requirements Analysis

  • • Evaluate data distribution
  • • Define privacy requirements
  • • Set performance metrics

2️⃣ Architecture Design

  • • Choose federation type
  • • Design communication protocol
  • • Formulate security strategies

3️⃣ System Implementation

  • • Deploy federated framework
  • • Integrate clients
  • • Monitoring and operations

Protect Privacy, Share Intelligence

Federated learning enables the flow of data value and intelligent collaboration while protecting privacy.

Learn More