Federated Learning — AI Training Without Sharing Data

A hospital wants AI for diagnostics but cannot share patient data. Federated learning: the model goes to the data, not the data to the model.

How It Works¶

Each participant trains locally. Only model updates (gradients) are sent to the center, not data. A central server aggregates them.

Types¶

• Cross-device: Millions of mobile devices (Google Keyboard)
• Cross-silo: Organizations collaborating (hospitals, banks)

Frameworks¶

Flower: Framework-agnostic, most popular. PySyft: Privacy-first. NVIDIA FLARE: Enterprise healthcare.

Federated Learning Is the Future of Privacy-Preserving AI¶

For healthcare, finance, and government, it’s the only path to AI without centralizing data.