Security of federated learning for cloud‐edge intelligence collaborative computing

International Journal of Intelligent SystemsVolume 37, Issue 11 p. 9290-9308 RESEARCH ARTICLE Security of federated learning for cloud-edge intelligence collaborative computing Jie Yang, Jie Yang School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorJun Zheng, Jun Zheng School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorZheng Zhang, Zheng Zhang School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorQ. I. Chen, Q. I. Chen Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorDuncan S. Wong, Duncan S. Wong Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorYuanzhang Li, Corresponding Author Yuanzhang Li popular@bit.edu.cn orcid.org/0000-0002-1931-366X School of Computer Science, Beijing Institute of Technology, Beijing, China Correspondence Yuanzhang Li, School of Computer Science, Beijing Institute of Technology, 100081 Beijing, China. Email: popular@bit.edu.cnSearch for more papers by this author Jie Yang, Jie Yang School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorJun Zheng, Jun Zheng School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorZheng Zhang, Zheng Zhang School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorQ. I. Chen, Q. I. Chen Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorDuncan S. Wong, Duncan S. Wong Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorYuanzhang Li, Corresponding Author Yuanzhang Li popular@bit.edu.cn orcid.org/0000-0002-1931-366X School of Computer Science, Beijing Institute of Technology, Beijing, China Correspondence Yuanzhang Li, School of Computer Science, Beijing Institute of Technology, 100081 Beijing, China. Email: popular@bit.edu.cnSearch for more papers by this author First published: 18 August 2022 https://doi.org/10.1002/int.22992Read 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 onFacebookTwitterLinkedInRedditWechat Abstract Federated Learning (FL) is one of the key technologies to solve privacy protection for cloud-edge intelligent collaborative computing, and its security and privacy issues have attracted extensive attention from academia and industry. FL is a distributed privacy protection framework. Multiple edged nodes or servers jointly train a machine learning model by sharing model parameters without exchanging local data. However, there are still many security risks and privacy threats in FL in edge-cloud collaborative computing. In this paper, we mainly discuss the security and privacy challenges on FL in collaborative computing at the edge. First, we introduce the principle, classification, and threat model of FL in edge-cloud collaboration, which helps understand the challenges faced by edge-cloud collaborative computing. Second, privacy leakage attacks and poisoning attacks launched by adversaries or honest but curious actors are summarized and compared. Then, the problems existing on the attack method are summarized and analyzed. Finally, the future development direction of FL in the field of edge-cloud collaborative computing is further discussed. Open Research DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no new data were created or analyzed in this study. Volume37, Issue11November 2022Pages 9290-9308 RelatedInformation