Federated Learning (FL) has gained significant attention as a novel distributed machine learning paradigm that enables collaborative model training while preserving data privacy. However, traditional FL methods face challenges such as data heterogeneity, system heterogeneity, and labeled data scarcity.
A comprehensive survey of federated transfer learning: Challenges, methods and applications
