{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T16:11:41Z","timestamp":1776528701782,"version":"3.51.2"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["62271318"],"award-info":[{"award-number":["62271318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["21ZR1442700"],"award-info":[{"award-number":["21ZR1442700"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013105","name":"Shanghai Rising-Star Program","doi-asserted-by":"publisher","award":["22QA1406100"],"award-info":[{"award-number":["22QA1406100"]}],"id":[{"id":"10.13039\/501100013105","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001294"],"award-info":[{"award-number":["62001294"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20159"],"award-info":[{"award-number":["U20A20159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2024,1,1]]},"DOI":"10.1109\/jiot.2023.3285937","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T17:24:25Z","timestamp":1686763465000},"page":"444-461","source":"Crossref","is-referenced-by-count":9,"title":["Latency Minimization for Wireless Federated Learning With Heterogeneous Local Model Updates"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6995-1208","authenticated-orcid":false,"given":"Jingyang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1418-7465","authenticated-orcid":false,"given":"Yuanming","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6491-5750","authenticated-orcid":false,"given":"Min","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National University of Singapore, Queenstown, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7499-6256","authenticated-orcid":false,"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4383-9995","authenticated-orcid":false,"given":"Youlong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5234-9429","authenticated-orcid":false,"given":"Liqun","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC55385.2023.10118880"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Stat. (AISTATS)","author":"McMahan"},{"key":"ref3","article-title":"Towards federated learning at scale: System design","author":"Bonawitz","year":"2019","journal-title":"arXiv:1902.01046"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2019.1900271"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3007787"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/c2020-0-00624-9"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3126076"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.23919\/JCC.2020.09.009"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3183996"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3117481"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3460866.3461765"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3428152"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03583-3"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa1006"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref18","article-title":"A field guide to federated optimization","author":"Wang","year":"2021","journal-title":"arXiv:2107.06917"},{"key":"ref19","first-page":"4427","article-title":"Federated multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Smith"},{"key":"ref20","first-page":"1","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Fallah"},{"key":"ref21","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Dinh"},{"key":"ref22","first-page":"6","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. Int. Conf. Learn. Rep. (ICLR)","author":"Li"},{"key":"ref23","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2018","journal-title":"arXiv:1812.06127"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3115952"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3065748"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3090323"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2961673"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3108197"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3015489"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000045"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3086116"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3099505"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500875"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3052681"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3125282"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036948"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3042530"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3025446"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118436"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3037554"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3021177"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.3035770"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2952051"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118401"},{"key":"ref49","first-page":"91","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Wang"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118431"},{"key":"ref51","first-page":"3403","article-title":"Towards flexible device participation in federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Stat. (AISTATS)","author":"Ruan"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT50566.2022.9834342"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC51304.2022.9833989"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036971"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3201117"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2940820"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900119"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3126057"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3096076"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2989580"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2974748"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2981904"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1137\/110830629"},{"key":"ref65","article-title":"On the convergence of local descent methods in federated learning","author":"Haddadpour","year":"2019","journal-title":"arXiv:1910.14425"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/BF02124750"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-30528-9_7"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974997"},{"key":"ref69","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref70","article-title":"Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks","author":"Morafah","year":"2022","journal-title":"arXiv:2209.15595"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/10375273\/10153411.pdf?arnumber=10153411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:09:08Z","timestamp":1705021748000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10153411\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":70,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2023.3285937","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,1]]}}}