Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning. Firstly, we introduce the study history and terminology definition of this area. Then, we comprehensively review three basic lines of research: generalization, robustness, and fairness, by introducing their respective background concepts, task settings, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out several open issues in this field and suggest opportunities for further research. We also provide a public website to continuously track developments in this fast advancing field: https://github.com/WenkeHuang/MarsFL.

联邦学习是一种有前景的在不同方参与者之间保护隐私的合作范例，本文系统地概述了对联邦学习的重要和最新研究进展，包括研究历史、术语定义、泛化、鲁棒性和公平性等方面，并提出了进一步研究的机会和公开问题。

联邦学习的推广、鲁棒性、公正性: 调查和基准