Federated Learning — a distributed training approach where models are trained across many decentralized devices (phones, hospitals, banks) without sharing raw data, preserving privacy.

How It Works

1. Server sends global model to N client devices 2. Each device trains on its local data for a few epochs 3. Devices send only model updates (gradients/weights) back to server — NOT the raw data 4. Server aggregates updates (FedAvg: weighted average) → new global model 5. Repeat for many rounds

Why Federated Learning?

• Privacy: Raw data never leaves the device (medical records, financial data, personal messages)
• Regulation: GDPR, HIPAA compliance — data can't be centralized
• Scale: Billions of mobile devices as training nodes (Google Keyboard predictions trained this way)

Challenges

• Non-IID data: Each device has different data distribution (heterogeneous)
• Communication cost: Sending model updates is expensive over mobile networks
• Stragglers: Some devices are slow or drop out
• Privacy attacks: Gradient inversion can partially reconstruct training data

Real Applications

• Google Gboard: Next-word prediction trained on-device
• Apple: Siri improvements without collecting voice data
• Healthcare: Multi-hospital medical imaging models

Federated learning makes it possible to train AI on sensitive data that could never be collected into a single dataset.