Federated Learning: Challenges, Methods, and Future Directions 论文

详细信息

发表期刊/会议

IEEE Signal Processing Magazine

发表日期

2020-05-01

发表年份

2020

摘要

Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.