Summary:
- This article provides a comprehensive overview of Federated Learning, a decentralized machine learning technique that allows models to train across multiple devices without exchanging raw data.
- It covers key architectural components, privacy-preserving benefits, and practical use cases for implementing this privacy-centric approach in modern AI development.