Robot target recognition using deep federated learning

International Journal of Intelligent SystemsVolume 36, Issue 12 p. 7754-7769 RESEARCH ARTICLE Robot target recognition using deep federated learning Bin Xue, Corresponding Author Bin Xue [email protected] orcid.org/0000-0001-7500-8128 National University of Defense Technology, School of Information and Communication, Xi'an, China Xi'an Jiaotong University, School of Computer Science and Technology, Xi'an, China Correspondence Bin Xue, National University of Defense Technology, 710076 Xi'an, China. Email: [email protected]Search for more papers by this authorYi He, Yi He orcid.org/0000-0002-3009-4357 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorFeng Jing, Feng Jing orcid.org/0000-0001-7061-8162 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorYimeng Ren, Yimeng Ren orcid.org/0000-0001-8142-5614 Renmin University of China, School of Statistics, Beijing, ChinaSearch for more papers by this authorLingling Jiao, Lingling Jiao National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorYang Huang, Yang Huang National University of Defense Technology, School of Electronic Science, Xi'an, ChinaSearch for more papers by this author Bin Xue, Corresponding Author Bin Xue [email protected] orcid.org/0000-0001-7500-8128 National University of Defense Technology, School of Information and Communication, Xi'an, China Xi'an Jiaotong University, School of Computer Science and Technology, Xi'an, China Correspondence Bin Xue, National University of Defense Technology, 710076 Xi'an, China. Email: [email protected]Search for more papers by this authorYi He, Yi He orcid.org/0000-0002-3009-4357 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorFeng Jing, Feng Jing orcid.org/0000-0001-7061-8162 National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorYimeng Ren, Yimeng Ren orcid.org/0000-0001-8142-5614 Renmin University of China, School of Statistics, Beijing, ChinaSearch for more papers by this authorLingling Jiao, Lingling Jiao National University of Defense Technology, School of Information and Communication, Xi'an, ChinaSearch for more papers by this authorYang Huang, Yang Huang National University of Defense Technology, School of Electronic Science, Xi'an, ChinaSearch for more papers by this author First published: 12 August 2021 https://doi.org/10.1002/int.22606Citations: 5Read 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 Abstract Robot target recognition is a critical and fundamental machine vision task. In this paper, InVision, a robot target recognition approach is proposed using deep federated learning. Particularly, deep geometric learning is developed to improve the perception capabilities of convolutional neural networks, and promote the representation maps' resolutions while achieving good recognition performance. Moreover, federated metric learning is constructed to protect user data privacy across multiple devices and relieve the problem of inadequate available labeled training data. To improve the speed of the recognition system, a lightweight deep neural network is presented. Extensive experiments are performed, showing that InVision significantly outperforms the outstanding comparison approaches. Citing Literature Volume36, Issue12December 2021Pages 7754-7769 RelatedInformation