Contribution‐based Federated Learning client selection

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Contribution-based Federated Learning client selection Weiwei Lin, Corresponding Author Weiwei Lin linww@scut.edu.cn orcid.org/0000-0001-6876-1795 School of Computer Science and Engineering, South China University of Technology, Guangzhou, China Pengcheng Laboratory, Shenzhen, China Correspondence Weiwei Lin, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. Email: linww@scut.edu.cn Bo Liu, School of Computer Science, South China Normal University, Guangzhou, China. Email: liugubin530@126.comSearch for more papers by this authorYinhai Xu, Yinhai Xu School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this authorBo Liu, Corresponding Author Bo Liu liugubin530@126.com School of Computer Science, South China Normal University, Guangzhou, China Correspondence Weiwei Lin, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. Email: linww@scut.edu.cn Bo Liu, School of Computer Science, South China Normal University, Guangzhou, China. Email: liugubin530@126.comSearch for more papers by this authorDongdong Li, Dongdong Li School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this authorTiansheng Huang, Tiansheng Huang School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this authorFang Shi, Fang Shi School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this author Weiwei Lin, Corresponding Author Weiwei Lin linww@scut.edu.cn orcid.org/0000-0001-6876-1795 School of Computer Science and Engineering, South China University of Technology, Guangzhou, China Pengcheng Laboratory, Shenzhen, China Correspondence Weiwei Lin, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. Email: linww@scut.edu.cn Bo Liu, School of Computer Science, South China Normal University, Guangzhou, China. Email: liugubin530@126.comSearch for more papers by this authorYinhai Xu, Yinhai Xu School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this authorBo Liu, Corresponding Author Bo Liu liugubin530@126.com School of Computer Science, South China Normal University, Guangzhou, China Correspondence Weiwei Lin, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China. Email: linww@scut.edu.cn Bo Liu, School of Computer Science, South China Normal University, Guangzhou, China. Email: liugubin530@126.comSearch for more papers by this authorDongdong Li, Dongdong Li School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this authorTiansheng Huang, Tiansheng Huang School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this authorFang Shi, Fang Shi School of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSearch for more papers by this author First published: 01 April 2022 https://doi.org/10.1002/int.22879Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Federated Learning (FL), as a privacy-preserving machine learning paradigm, has been thrusted into the limelight. As a result of the physical bandwidth constraint, only a small number of clients are selected for each round of FL training. However, existing client selection solutions (e.g., the vanilla random selection) typically ignore the heterogeneous data value of the clients. In this paper, we propose the contribution-based selection algorithm (Contribution-Based Exponential-weight algorithm for Exploration and Exploitation, CBE3), which dynamically updates the selection weights according to the impact of clients' data. As a novel component of CBE3, a scaling factor, which helps maintain a good balance between global model accuracy and convergence speed, is proposed to improve the algorithm's adaptability. Theoretically, we proved the regret bound of the proposed CBE3 algorithm, which demonstrates performance gaps between the CBE3 and the optimal choice. Empirically, extensive experiments conducted on Non-Independent Identically Distributed data demonstrate the superior performance of CBE3—with up to 10% accuracy improvement compared with K-Center and Greedy and up to 100% faster convergence compared with the Random algorithm. CONFLICTS OF INTEREST The authors declare no conflicts of interest. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation