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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.