联邦学习的公平性研究综述.

Federated learning uses data from multiple participants to collaboratively train global models and has played an increasingly important role in recent years in facilitating inter-firm data collaboration. On the other hand, the federal learning training paradigm often faces the dilemma of insufficient data, so it is important to provide assurance of fairness to motivate more participants to contribute their valuable resources. This paper illustrates the issue of fairness in federated learning. Firstly, three classifications of fairness based on different equity goals, from model performance balance, contribution assessment equity, and elimination of group discrimination are proposed, and then we provide indepth introduction and comparison of existing fairness promotion methods, aiming to help researchers develop new fairness promotion methods. Finally, by dissecting the needs in the process of federal learning implementation, five directions for future federated learning fairness research are proposed. [ABSTRACT FROM AUTHOR]