{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T20:54:01Z","timestamp":1774558441844,"version":"3.50.1"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62133003"],"award-info":[{"award-number":["62133003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Consumer Electron."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/tce.2025.3642305","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T18:32:24Z","timestamp":1765391544000},"page":"1802-1810","source":"Crossref","is-referenced-by-count":0,"title":["Decentralized Federated Learning for Time Series Prediction in Electric Vehicle Charging Ecosystems"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0827-5661","authenticated-orcid":false,"given":"Jingfei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9172-4669","authenticated-orcid":false,"given":"Danya","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4090-8497","authenticated-orcid":false,"given":"Tao","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2025.3551511"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2024.3413052"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2024.3446859"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.4018\/979-8-3693-2611-4.ch013"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2374993"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph14121509"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3377454"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3401850"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"key":"ref10","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3385440"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3347170"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3323206"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3338464"},{"key":"ref15","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd Mach. Learn. Syst. Conf.","author":"Li"},{"key":"ref16","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. NeurIPS Conf.","volume":"33","author":"Fallah"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3369655"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3407584"},{"key":"ref19","article-title":"Enhanced decentralized federated learning based on consensus in connected vehicles","author":"Liu","year":"2022","journal-title":"arXiv:2209.10722"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3152445"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1360\/nso\/20220043"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2024.105951"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3485801"},{"key":"ref24","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019","journal-title":"arXiv:1903.03934"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.02.012"},{"key":"ref26","first-page":"73258","article-title":"FLuID: Mitigating stragglers in federated learning using invariant dropout","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijtst.2025.04.007"},{"key":"ref28","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"Proc. 25th Int. Conf. Artif. Intell. Statist.","author":"Nguyen"},{"key":"ref29","article-title":"FedFa: A fully asynchronous training paradigm for federated learning","author":"Xu","year":"2024","journal-title":"arXiv:2404.11015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2024.3484651"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3424444"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.107683"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04874-4"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3039815"}],"container-title":["IEEE Transactions on Consumer Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/30\/11456295\/11296852.pdf?arnumber=11296852","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:51:11Z","timestamp":1774554671000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11296852\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":34,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tce.2025.3642305","relation":{},"ISSN":["0098-3063","1558-4127"],"issn-type":[{"value":"0098-3063","type":"print"},{"value":"1558-4127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}