Document worth reading: “Federated Learning: Challenges, Methods, and Future Directions”

Federated learning entails teaching statistical fashions over distant devices or siloed info amenities, akin to cellphones or hospitals, whereas retaining info localized. Training in heterogeneous and in all probability giant networks introduces novel challenges that require a fundamental departure from commonplace approaches for large-scale machine learning, distributed optimization, and privacy-preserving info analysis. In this textual content, we concentrate on the distinctive traits and challenges of federated learning, current a broad overview of current approaches, and outline quite a lot of directions of future work that are associated to a wide range of evaluation communities. Federated Learning: Challenges, Methods, and Future Directions