{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:27:30Z","timestamp":1775744850504,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/jiot.2025.3598127","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T18:03:32Z","timestamp":1755021812000},"page":"1-1","source":"Crossref","is-referenced-by-count":4,"title":["GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2331-604X","authenticated-orcid":false,"given":"Shijie","family":"Na","sequence":"first","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6868-1971","authenticated-orcid":false,"given":"Yuzhi","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3975-8500","authenticated-orcid":false,"given":"Siu-ming","family":"Yiu","sequence":"additional","affiliation":[{"name":"Pokfulam, The University of Hong Kong, Department of Computer Science, Hong Kong, Hong Kong"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100657"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10969-y"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599500"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/462"},{"key":"ref5","first-page":"1","article-title":"Diverse client selection for federated learning via submodular maximization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Balakrishnan"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-022-01078-1"},{"key":"ref7","first-page":"1","article-title":"An improved analysis of stochastic gradient descent with momentum","volume-title":"Proc. 34th Annu. Conf. Neural Inf. Process. Syst. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Liu"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00986"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3608251.3608281"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120295"},{"key":"ref11","first-page":"151","article-title":"Combinatorial multi-armed bandit: General framework and applications","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref12","first-page":"1","article-title":"Stochastic multi-armed-bandit problem with non-stationary rewards","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Besbes"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2013.2277869"},{"key":"ref14","first-page":"1","article-title":"LEAF: A benchmark for federated settings","volume-title":"Proc. Workshop Fed. Learn. Data Privacy Confid.","author":"Caldas"},{"key":"ref15","volume-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky","year":"2009"},{"key":"ref16","first-page":"10351","article-title":"Towards understanding biased client selection in federated learning","volume-title":"Proc. 25th Int. Conf. Artif. Intell. Statist.","author":"Cho"},{"key":"ref17","first-page":"65525","article-title":"Heterogeneity-guided client sampling: Towards fast and efficient non-IID federated learning","volume-title":"Proc. 38th Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3294295"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3552326.3567485"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3028742"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3232891"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299573"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00182"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3134647"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2024.110663"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3172113"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.23919\/WiOpt52861.2021.9589776"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036952"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/int.22879"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.05.003"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517017"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3210950"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3606017"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3186960"},{"key":"ref37","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","volume-title":"Proc. 15th {USENIX} Symp. Oper. Syst. Design Implement. ({OSDI} 21)","author":"Lai"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3265564"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.043684"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103462"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3676536.3676815"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaaiss.v3i1.31227"},{"key":"ref43","article-title":"Swarm intelligence-driven client selection for federated learning in cybersecurity applications","author":"Khan","year":"2024","journal-title":"arXiv:2411.18877"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/6702522\/11123576.pdf?arnumber=11123576","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T04:42:35Z","timestamp":1761367355000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11123576\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3598127","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}