电子学报2023，Vol.51Issue(10)：2985-3010,26.DOI:10.12263/DZXB.20230139
面向可信联邦学习公平性的研究综述
Fairness in Trustworthy Federated Learning:A Survey
摘要
Abstract
Federated learning is a distributed machine learning paradigm that facilitates data sharing and collaborative computing among multiple participants.Currently,research on federated learning primarily focuses on performance im-provement and privacy protection.With the emergence of trustworthy artificial intelligence,the research on trustworthy fed-erated learning methods has gained more attention,and ensuring fairness in federated learning is one of the main challeng-es.Improving the fairness of federated learning can motivate the enthusiasm of clients and ensure the sustainability of feder-ated learning training.However,due to the heterogeneity of data and devices in federated learning,traditional federated learning methods may lead to significant performance differences between clients,which may hinder fairness among all par-ticipants and significantly impact the motivation of users to participate in federated learning.Based on this,this paper pro-vides a comprehensive review of the research methods of fairness in federated learning.Firstly,we categorize the main re-search directions of fairness in federated learning,elaborates the definition and compares the evaluation criteria of fairness in each direction.Next,we discuss the challenges and main solutions for improving fairness in federated learning in each di-rection.Then,we summarize the commonly used datasets,experimental scenarios,and fairness evaluation metrics in the study of fairness.Finally,we prospectively explore the future research directions and development trends of fairness in fed-erated learning.关键词
可信赖/联邦学习/公平性/数据异构/协同计算Key words
trustworthy/federated learning/fairness/data heterogeneity/collaborative computing分类
信息技术与安全科学引用本文复制引用
陈颢瑜,李浥东,张洪磊,陈乃月..面向可信联邦学习公平性的研究综述[J].电子学报,2023,51(10):2985-3010,26.基金项目
国家自然科学基金(No.U1934220,No.U2268203)National Natural Science Foundation of China(No.U 1934220,No.U2268203) （No.U1934220,No.U2268203）
