Federated learning (FL) is an approach to machine learning (ML) where model training data is not stored in a central location and computation is distributed, enabling secure and privacy-preserving multi-party collaboration. FL can be used in healthcare to rapidly and securely develop new ML models from heterogeneous data sets and diverse sources, all while avoiding […]