Federated learning is a decentralized approach to training machine learning models where data remains on local devices rather than being centralized in one server. The model is trained across multiple devices or servers, with each contributing updates, ensuring privacy and reducing data transfer. It's commonly used in applications like personalized mobile experiences without sharing sensitive user data.