{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:36:04Z","timestamp":1771706164397,"version":"3.50.1"},"reference-count":42,"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"}],"funder":[{"name":"Shijiazhuang Science and Technology Plan Project","award":["241791277A"],"award-info":[{"award-number":["241791277A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/jiot.2025.3601816","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:02:27Z","timestamp":1755910947000},"page":"1-1","source":"Crossref","is-referenced-by-count":1,"title":["Heterogeneous Vehicular Selection for Adaptive Federated Learning: A Cost-Optimized Approach"],"prefix":"10.1109","author":[{"given":"Shuang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of College of Engineering, Hebei Normal University, Shijiazhuang, China"}]},{"given":"Songwen","family":"Gu","sequence":"additional","affiliation":[{"name":"School of College of Engineering, Hebei Normal University, Shijiazhuang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4401-8143","authenticated-orcid":false,"given":"Hua","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of College of Engineering, Hebei Normal University, Shijiazhuang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0386-0025","authenticated-orcid":false,"given":"Junpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of College of Engineering, Hebei Normal University, Shijiazhuang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5380-3719","authenticated-orcid":false,"given":"Huilong","family":"Jin","sequence":"additional","affiliation":[{"name":"School of College of Engineering, Hebei Normal University, Shijiazhuang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3437-7606","authenticated-orcid":false,"given":"Maher","family":"Guizani","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, University of Texas Arlington, Texas, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3492326"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10278823"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-023-2972-9"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2024.3487955"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.3035770"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops57953.2023.10283698"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020721"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2024.3359860"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3099597"},{"key":"ref10","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Stat.","author":"McMahan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3210365"},{"key":"ref12","article-title":"On the convergence of FedAvg on non-iid data","author":"Li","year":"2019","journal-title":"arXiv:1907.02189"},{"key":"ref13","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","volume":"2","author":"Li"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2022.3205475"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3108197"},{"key":"ref17","first-page":"10752","article-title":"On convergence of FedProx: Local dissimilarity invariant bounds, non-smoothness and beyond","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Yuan"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3146399"},{"key":"ref20","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2995162"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3556548.3559633"},{"key":"ref23","article-title":"Optimizing federated learning by entropy-based client selection","author":"Lutz","year":"2024","journal-title":"arXiv:2411.01240"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3056919"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICME55011.2023.00063"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-024-02313-1"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3320123"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2025.3527202"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2024.110663"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICC51166.2024.10623087"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3217271"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3017474"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-3581-9_1"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3303492"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3291701"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3347912"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2023.3297793"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121552"},{"key":"ref40","volume-title":"Classification of MNIST Handwritten Digit Database Using Neural Network","author":"Zhu","year":"2018"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-023-10011-4"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/6702522\/11134397.pdf?arnumber=11134397","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T04:44:28Z","timestamp":1761367468000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11134397\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3601816","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}