CRFL: A novel federated learning scheme of client reputation assessment via local model inversion

International Journal of Intelligent SystemsVolume 37, Issue 8 p. 5457-5471 RESEARCH ARTICLE CRFL: A novel federated learning scheme of client reputation assessment via local model inversion Jiamin Zheng, Jiamin Zheng School of E-Business and Logistics, Beijing Technology and Business University, Beijing, ChinaSearch for more papers by this authorTeng Huang, Corresponding Author Teng Huang huangteng1220@buaa.edu.cn orcid.org/0000-0001-7261-6398 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Correspondence Teng Huang, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, China. Email: huangteng1220@buaa.edu.cnSearch for more papers by this authorJiahui Huang, Jiahui Huang Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author Jiamin Zheng, Jiamin Zheng School of E-Business and Logistics, Beijing Technology and Business University, Beijing, ChinaSearch for more papers by this authorTeng Huang, Corresponding Author Teng Huang huangteng1220@buaa.edu.cn orcid.org/0000-0001-7261-6398 Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Correspondence Teng Huang, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, China. Email: huangteng1220@buaa.edu.cnSearch for more papers by this authorJiahui Huang, Jiahui Huang Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this author First published: 07 June 2022 https://doi.org/10.1002/int.22914Read 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) is gradually becoming a key learning paradigm in Privacy-preserving Machine Learning (ML) systems. In FL, a large number of clients cooperate with a central server to learn a shared model without sharing their own data sets. However, since there is a great disparity between the client data sets, standard FL is often hard to tune and suffers from performance degradation due to the inharmony among local models. To this end, in this paper we propose a novel FL scheme, termed client reputation federated learning (CRFL), which dynamically assesses the reputation of the clients participating in FL. Our method leverages techniques from model explanation, and aims at precisely measure each client's impact to the global model. To be specific, we first calculate the saliency-weighted variance on pixelwise relevance scores as the quality factor of a single sample. Then we extract activation function values at the last hidden layer to compute the divergence factor of individual data set. Finally, the server integrates these two factors as an assessment of the client reputation. By leveraging such assessment, CRFL can dynamically adjust the weights of the clients in each aggregation round, thus leading to a significant improvement over the baseline method in terms of model accuracy and convergence rate. Intensive experiments are conducted on the MNIST and CIFAR-10 data sets, and experimental results demonstrate the efficacy of the proposed method. CONFLICT OF INTEREST The authors declare no conflict of interest. Open Research DATA AVAILABILITY STATEMENT The data used to support the findings of this study are available from the corresponding author upon request. Volume37, Issue8August 2022Pages 5457-5471 RelatedInformation