面向可信联邦学习公平性的研究综述

陈颢瑜; 李浥东; 张洪磊; 陈乃月

doi:10.12263/DZXB.20230139

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面向可信联邦学习公平性的研究综述

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- 面向可信联邦学习公平性的研究综述
- Fairness in Trustworthy Federated Learning: A Survey
- 电子学报 2023年51卷第10期页码：2985-3010
- 作者机构：
  
  1.北京交通大学计算机与信息技术学院，北京100044
  2.交通大数据与人工智能教育部重点实验室，北京100044
- 作者简介：
  
  [ "陈颢瑜男，1995年1月出生于河南省洛阳市. 北京交通大学计算机与信息技术学院博士研究生. 主要研究方向为隐私计算与可信智能. E-mail: hychen95@bjtu.edu.cn" ]
  [ "李浥东（通讯作者）男，1982年2月出生于山西省太原市. 北京交通大学计算机与信息技术学院教授. 主要研究方向为大数据分析与安全、数据隐私保护与先进计算." ]
  [ "张洪磊男，1993年7月出生于河北省邢台市. 北京交通大学计算机与信息技术学院博士研究生. 主要研究方向为推荐系统与隐私保护.E-mail: honglei.zhang@bjtu.edu.cn" ]
  [ "陈乃月女，1989年8月出生于河北省唐山市. 北京交通大学计算机与信息技术学院讲师. 主要研究方向为社交网络、数据挖掘与联邦学习.E-mail: nychen@bjtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(U1934220;U2268203)
- DOI：10.12263/DZXB.20230139
  中图分类号： TP309;TP399
- 收稿：2023-02-17，
  
  修回：2023-08-31，
  
  纸质出版：2023-10-25
- 稿件说明：
移动端阅览
陈颢瑜,李浥东,张洪磊等.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.

CHEN Hao-yu,LI Yi-dong,ZHANG Hong-lei,et al.Fairness in Trustworthy Federated Learning: A Survey[J].ACTA ELECTRONICA SINICA,2023,51(10):2985-3010.
陈颢瑜,李浥东,张洪磊等.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. DOI： 10.12263/DZXB.20230139.

CHEN Hao-yu,LI Yi-dong,ZHANG Hong-lei,et al.Fairness in Trustworthy Federated Learning: A Survey[J].ACTA ELECTRONICA SINICA,2023,51(10):2985-3010. DOI： 10.12263/DZXB.20230139.

摘要

联邦学习能够促进多方参与者之间的数据共享和协同计算，其已经成为一种流行的分布式机器学习范式.联邦学习目前的研究主要集中在性能提升和隐私保护方面.近年来，随着可信人工智能研究的深入，可信联邦学习的研究也受到越来越多的关注.其中，保证联邦学习的公平性是面临的关键问题之一.提升联邦学习的公平性能够保证客户端参与的积极性和联邦学习训练的可持续性.然而，由于联邦学习中通常存在着数据异构性和设备异构性，传统的联邦学习方法会导致客户端之间具有很大的差异，无法保证所有参与者之间的公平，这会极大地影响用户参与联邦学习的动力.基于此，对近年来联邦学习公平性的研究方法进行全面归纳梳理与深度探讨分析.首先对当前联邦学习公平性研究的主要方向进行划分，并对每个方向的公平性定义与评价标准进行了解释及对比.随后详细探讨了联邦学习公平性不同方向面临的挑战和主要解决方案.最后对联邦学习公平性研究中常用的数据集、实验场景设置和公平评价指标进行了归纳梳理，并对未来研究方向与发展趋势进行探讨和展望.

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 improvement and privacy protection. With the emergence of trustworthy artificial intelligence

the research on trustworthy federated learning methods has gained more attention

and ensuring fairness in federated learning is one of the main challenges. Improving the fairness of federated learning can motivate the enthusiasm of clients and ensure the sustainability of federated 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 participants and significantly impact the motivation of users to participate in federated learning. Based on this

this paper provides a comprehensive review of the research methods of fairness in federated learning. Firstly

we categorize the main research 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 direction. 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 federated learning.

关键词

Keywords

references

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