Federated Learning: Privacy‑Preserving Machine Learning at Scale
Traditional ML requires centralizing data in one repository—raising privacy, security, and compliance concerns. Federated learning flips the script: models are trained locally on distributed devices or servers, and only weight updates (never raw data) are shared, enabling collaborative intelligence while keeping user data on‑device. 1. How Federated Learning Works 2. Real‑World Applications 3. Privacy & … Read more