Federated Learning — Complete Guide

Federated learning trains models across decentralized devices without centralizing data. Essential for privacy-sensitive applications.

How It Works

Traditional ML: Data → Central server → Model
Federated ML: Model → Devices → Updates → Server

Round 1:
├─ Server sends model to devices
├─ Each device trains on local data
├─ Devices send gradient updates
└─ Server aggregates updates

Round 2, 3, ...:
├─ Repeat until convergence
└─ Final model trained on all data without sharing it

Challenges

Non-IID data: Different devices have different data distributions
Communication: Gradient updates can be large
Stragglers: Some devices are slower
Privacy: Updates can leak information

Solutions:
├─ Differential privacy: Add noise to updates
├─ Secure aggregation: Encrypt updates
├─ Compression: Reduce update size
└─ Asynchronous updates: Handle stragglers

Key Takeaways

1. Federated learning trains models without sharing data
2. Differential privacy protects individual data points
3. Secure aggregation prevents server from seeing updates
4. Non-IID data is the main technical challenge
5. Used by Apple, Google, healthcare for privacy
6. Communication efficiency is critical for mobile devices
7. Federated averaging is the standard algorithm
8. Federated learning enables collaborative AI while preserving privacy