Federated Learning#
Federated Learning (FL) enables decentralized model training across multiple clients without sharing raw data, enhancing privacy. Each client trains a local model and shares updates (not data) with a central server, which aggregates these updates to improve a global model. This approach is scalable, communication-efficient, and robust, making it ideal for sensitive data applications like healthcare and finance. Key challenges include data heterogeneity, communication overhead, and security risks, but FL remains a powerful tool for collaborative learning across distributed data sources.