大数据2024，Vol.10Issue(1)：62-85,24.DOI:10.11959/j.issn.2096-0271.2022088
联邦学习的公平性研究综述
A survey on the fairness of federated learning
摘要
Abstract
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 in-depth 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.关键词
联邦学习/公平性/表现均衡/贡献衡量Key words
federated learning/fairness/balance in performance/measure of contributions分类
管理科学引用本文复制引用
朱智韬,司世景,王健宗,程宁,孔令炜,黄章成,肖京..联邦学习的公平性研究综述[J].大数据,2024,10(1):62-85,24.基金项目
广东省重点领域研发计划"新一代人工智能"重大专项(No.2021B0101400003) The Key Research and Development Program of Guangdong Province(No.2021B0101400003) （No.2021B0101400003）
