Federated Learning: Challenges, Methods, and Future Directions

This article surveys federated learning, a paradigm in which statistical models are trained over remote devices or siloed data centers, such as mobile phones or hospitals, while the underlying data remains localized rather than being collected centrally. The authors emphasize that training in heterogeneous and potentially massive networks introduces novel challenges that call for a fundamental departure from standard approaches to large-scale machine learning, distributed optimization, and privacy-preserving data analysis.

The paper discusses the unique characteristics and challenges of federated learning, provides a broad overview of current approaches to the problem, and outlines several directions for future work. Because these directions are relevant to a wide range of research communities, the article serves as a reference framing of the field's open problems and methods, helping to orient subsequent research on learning from decentralized, privacy-sensitive data.