{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:25:41Z","timestamp":1780773941957,"version":"3.54.1"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100013290","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2018YFB1800800"],"award-info":[{"award-number":["2018YFB1800800"]}],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61731018"],"award-info":[{"award-number":["61731018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Key Laboratory of Big Data Computation Theories and Methods"},{"DOI":"10.13039\/501100012271","name":"Shenzhen Fundamental Research and Discipline Layout project","doi-asserted-by":"publisher","award":["JCYJ20190813171003723"],"award-info":[{"award-number":["JCYJ20190813171003723"]}],"id":[{"id":"10.13039\/501100012271","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61731018"],"award-info":[{"award-number":["61731018"]}],"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":["62071409"],"award-info":[{"award-number":["62071409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tsp.2021.3097211","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T19:58:23Z","timestamp":1628884703000},"page":"4486-4501","source":"Crossref","is-referenced-by-count":31,"title":["Distributed Stochastic Consensus Optimization With Momentum for Nonconvex Nonsmooth Problems"],"prefix":"10.1109","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2604-3597","authenticated-orcid":false,"given":"Zhiguo","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1349-2764","authenticated-orcid":false,"given":"Tsung-Hui","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-1423","authenticated-orcid":false,"given":"Jian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Quan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Supplementary material for distributed consensus optimization with momentum for nonconvex nonsmooth problems","author":"wang","year":"0"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1287\/moor.12.3.474"},{"key":"ref33","article-title":"Mini-batch stochastic ADMMs for nonconvex nonsmooth optimization","author":"huang","year":"2018"},{"key":"ref32","article-title":"Distributed stochastic consensus optimization with momentum for nonconvex nonsmooth problems","author":"wang","year":"2020"},{"key":"ref31","first-page":"7184","article-title":"On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization","author":"yu","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/410"},{"key":"ref37","article-title":"Scheduled restart momentum for accelerated stochastic gradient descent","author":"wang","year":"2020"},{"key":"ref36","first-page":"1662","article-title":"Communication-efficient distributed optimization in networks with gradient tracking and variance reduction","author":"li","year":"2020","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref35","first-page":"701","article-title":"Learning with non-convex truncated losses by SGD","author":"xu","year":"2020","journal-title":"Proc Uncertainty Artif Intell"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-016-1034-2"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.3009363"},{"key":"ref40","article-title":"Introductory Lectures on Convex Optimization: A Basic Course.","author":"nesterov","year":"2004"},{"key":"ref11","article-title":"Distributed algorithms for composite optimization: Unified and tight convergence analysis","author":"xu","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2818081"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2016.2524588"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-97142-1_3"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-018-01357-w"},{"key":"ref16","first-page":"1529","article-title":"Prox-PDA: The proximal primal-dual algorithm for fast distributed nonconvex optimization and learning over networks","author":"hong","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2943230"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1137\/19M1242276"},{"key":"ref19","article-title":"A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization","author":"zhang","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2298712"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2367458"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2012.2209984"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3050858"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.2009515"},{"key":"ref29","article-title":"A sharp convergence rate analysis for distributed accelerated gradient methods","author":"li","year":"2018"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2970170"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2015.2461520"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1137\/14096668X"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2682318"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2304432"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2673815"},{"key":"ref22","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc Artif Intell Statist"},{"key":"ref21","first-page":"5330","author":"lian","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"karimireddy","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref41","author":"bertsekas","year":"1999","journal-title":"Nonlinear Programming"},{"key":"ref23","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/DSW.2019.8755807"},{"key":"ref25","first-page":"4848","article-title":"D$^2$: Decentralized training over decentralized data","author":"tang","year":"2018","journal-title":"Proc Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/9307529\/09488202.pdf?arnumber=9488202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:41Z","timestamp":1652194241000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9488202\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/tsp.2021.3097211","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"value":"1053-587X","type":"print"},{"value":"1941-0476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}