{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:07:52Z","timestamp":1776848872018,"version":"3.51.2"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CPS-2313109"],"award-info":[{"award-number":["CPS-2313109"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2212565"],"award-info":[{"award-number":["CNS-2212565"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"the Defense Advanced Research Projects Agency","doi-asserted-by":"crossref","award":["D22AP00168"],"award-info":[{"award-number":["D22AP00168"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"crossref","award":["N000142212305"],"award-info":[{"award-number":["N000142212305"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-24-1-0083"],"award-info":[{"award-number":["FA9550-24-1-0083"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/ton.2026.3673208","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T19:36:38Z","timestamp":1773257798000},"page":"4684-4699","source":"Crossref","is-referenced-by-count":0,"title":["A Hierarchical Gradient Tracking Algorithm for Mitigating Subnet-Drift in Fog Learning Networks"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5687-9466","authenticated-orcid":false,"given":"Evan","family":"Chen","sequence":"first","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2090-5512","authenticated-orcid":false,"given":"Shiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2771-3521","authenticated-orcid":false,"given":"Christopher G.","family":"Brinton","sequence":"additional","affiliation":[{"name":"Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM52122.2024.10621133"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229070"},{"key":"ref4","article-title":"Federated learning with quantized global model updates","author":"Mohammadi Amiri","year":"2020","journal-title":"arXiv:2006.10672"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3423673"},{"key":"ref6","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Karimireddy"},{"key":"ref7","first-page":"15750","article-title":"ProxSkip: Yes! local gradient steps provably lead to communication acceleration! Finally!","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mishchenko"},{"key":"ref8","article-title":"Decentralized deep learning with arbitrary communication compression","author":"Koloskova","year":"2019","journal-title":"arXiv:1907.09356"},{"key":"ref9","first-page":"5330","article-title":"Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent","volume-title":"Proc. Neural Inf. Process. Syst.","volume":"30","author":"Lian"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CDC51059.2022.9993258"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000410"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3167263"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2022.3143495"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.002.2400150"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118344"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT50566.2022.9834707"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2020.102403"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/s20030828"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2698164"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3332675"},{"key":"ref21","article-title":"Decentralized gradient tracking with local steps","author":"Liu","year":"2023","journal-title":"arXiv:2301.01313"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3075432"},{"key":"ref23","article-title":"On the convergence of local descent methods in federated learning","author":"Haddadpour","year":"2019","journal-title":"arXiv:1910.14425"},{"key":"ref24","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref26","article-title":"Optimal client sampling for federated learning","author":"Chen","year":"2020","journal-title":"arXiv:2010.13723"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036952"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.52591\/lxai2020071310"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796935"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1137\/19M1259973"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488756"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3213323"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20832"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3259431"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2024.3407716"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2024.3374591"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2016.2524588"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1137\/16M1084316"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2018.8636055"},{"key":"ref42","first-page":"11422","article-title":"An improved analysis of gradient tracking for decentralized machine learning","volume-title":"Proc. Conf. Neural Inf. Process. Syst.","author":"Koloskova"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2024.3383271"},{"key":"ref44","article-title":"Gradient and variable tracking with multiple local SGD for decentralized non-convex learning","author":"Ge","year":"2023","journal-title":"arXiv:2302.01537"},{"key":"ref45","article-title":"Balancing communication and computation in gradient tracking algorithms for decentralized optimization","author":"Berahas","year":"2023","journal-title":"arXiv:2303.14289"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488686"},{"key":"ref47","article-title":"Tackling data heterogeneity: A new unified framework for decentralized SGD with sample-induced topology","author":"Huang","year":"2022","journal-title":"arXiv:2207.03730"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/VTCSpring.2014.7022812"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2217338"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2016.7869154"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9"},{"key":"ref52","article-title":"Local SGD converges fast and communicates little","author":"Stich","year":"2018","journal-title":"arXiv:1805.09767"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s11081-007-9001-7"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3184770"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1561\/2200000051"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref58","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1137\/070704277"},{"key":"ref60","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019","journal-title":"arXiv:1907.02189"}],"container-title":["IEEE Transactions on Networking"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/10723154\/11317935\/11430675-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10723154\/11317935\/11430675.pdf?arnumber=11430675","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:10:17Z","timestamp":1776845417000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11430675\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/ton.2026.3673208","relation":{},"ISSN":["2998-4157"],"issn-type":[{"value":"2998-4157","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}