Federated Learning at Scale: Privacy-Preserving Distributed Training

2025-01-12

Train ML models across millions of devices without centralizing data. Essential for privacy but vulnerable to poisoning attacks.

Core Architecture

Secure Aggregation

Gradient Poisoning Defense

Differential Privacy

Production Deployment

Real-World Scale

Warnings ⚠️

Poisoning Attacks:

• Malicious client sends crafted gradients

• Can backdoor model or degrade performance

• Defense: Robust aggregation + anomaly detection

Privacy Leakage:

• Gradients can leak training data (membership inference)

• Defense: Differential privacy (adds noise)

• Tradeoff: Privacy vs accuracy

Communication Cost:

• 100M clients × model size × rounds = massive bandwidth

• Solution: Gradient compression, quantization

Related Chronicles: Decentralized AI Training Catastrophe (2051)

Tools: Flower, PySyft