Decentralized federated meta‐learning framework for few‐shot multitask learning

International Journal of Intelligent SystemsVolume 37, Issue 11 p. 8490-8522 RESEARCH ARTICLE Decentralized federated meta-learning framework for few-shot multitask learning Xiaoli Li, Xiaoli Li orcid.org/0000-0001-9113-7130 School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this authorYuzheng Li, Yuzheng Li School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this authorJining Wang, Jining Wang School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this authorChuan Chen, Corresponding Author Chuan Chen chenchuan@mail.sysu.edu.cn School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China Correspondence Chuan Chen, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China. Email: chenchuan@mail.sysu.edu.cnSearch for more papers by this authorLiu Yang, Liu Yang School of Computer Science and Technology, Tianjin University, Tianjin, ChinaSearch for more papers by this authorZibin Zheng, Zibin Zheng School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this author Xiaoli Li, Xiaoli Li orcid.org/0000-0001-9113-7130 School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this authorYuzheng Li, Yuzheng Li School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this authorJining Wang, Jining Wang School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this authorChuan Chen, Corresponding Author Chuan Chen chenchuan@mail.sysu.edu.cn School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China Correspondence Chuan Chen, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China. Email: chenchuan@mail.sysu.edu.cnSearch for more papers by this authorLiu Yang, Liu Yang School of Computer Science and Technology, Tianjin University, Tianjin, ChinaSearch for more papers by this authorZibin Zheng, Zibin Zheng School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaSearch for more papers by this author First published: 07 July 2022 https://doi.org/10.1002/int.22951Read 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 is increasingly attractive, however as the number of training samples on a single device is too small and the training tasks of the devices are different, it faces the few-shot multitask learning problem. Moreover, federated learning frameworks are usually vulnerable to malicious attacks of the central server and diverse clients. To address these problems, we propose a decentralized federated meta-learning framework (DFMLF) for few-shot multitask learning. In DFMLF, the devices take the rapid adaptation as objective and learn the meta-knowledge shared by tasks to deal with the few-shot multitask problem. In addition, DFMLF conducts cross-validation and secure aggregation mechanism by a small number of committee nodes, which not only eliminates the central server to avoid the security risks brought by the malicious central server, but also avoids the attack of malicious devices. Moreover, to address the extra communication cost brought by the committee strategy, we propose a communication-efficient method to make the training and aggregation carried out in parallel. We conduct extensive experiments based on real-world data sets, and the experimental results demonstrate the effectiveness, robustness, and efficiency of our framework. CONFLICTS OF INTEREST The authors declare no conflicts of interest. Volume37, Issue11November 2022Pages 8490-8522 RelatedInformation