Federated Learning

Federated Learning (FL) is a machine learning approach that allows a model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them centrally. This technique enables privacy-preserving model updates and is often used in applications where data privacy and security are critical, such as healthcare and mobile devices.