{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:40:08Z","timestamp":1782834008905,"version":"3.54.5"},"reference-count":90,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB3301500"],"award-info":[{"award-number":["2021YFB3301500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102391"],"award-info":[{"award-number":["62102391"]}],"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":["62132019"],"award-info":[{"award-number":["62132019"]}],"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":["61936015"],"award-info":[{"award-number":["61936015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Province Science Foundation for Youths","award":["BK20210122"],"award-info":[{"award-number":["BK20210122"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Parallel Distrib. Syst."],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1109\/tpds.2023.3303967","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T17:48:12Z","timestamp":1691689692000},"page":"2827-2842","source":"Crossref","is-referenced-by-count":16,"title":["Joint Model Pruning and Topology Construction for Accelerating Decentralized Machine Learning"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7338-0724","authenticated-orcid":false,"given":"Zhida","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0839-3892","authenticated-orcid":false,"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3831-4577","authenticated-orcid":false,"given":"Hongli","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9436-7924","authenticated-orcid":false,"given":"Lun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4679-6572","authenticated-orcid":false,"given":"Chunming","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8417-3256","authenticated-orcid":false,"given":"Liusheng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3151242"},{"key":"ref57","article-title":"Network automatic pruning: Start nap and take a nap","author":"zeng","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref56","article-title":"Learning both weights and connections for efficient neural networks","author":"han","year":"2015"},{"key":"ref15","first-page":"1791","article-title":"Accelerating gossip SGD with periodic global averaging","author":"chen","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref59","first-page":"5381","article-title":"A unified theory of decentralized SGD with changing topology and local updates","author":"koloskova","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","article-title":"Communication-efficient local decentralized SGD methods","author":"li","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysconle.2004.02.022"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107468"},{"key":"ref52","article-title":"Accurate, large minibatch SGD: Training imageNet in 1 hour","author":"goyal","year":"2017"},{"key":"ref11","first-page":"4387","article-title":"The non-IID data quagmire of decentralized machine learning","author":"hsieh","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref55","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"Int Conf Learn Representations"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100225"},{"key":"ref54","first-page":"5686","article-title":"Consensus control for decentralized deep learning","author":"kong","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref17","first-page":"7663","article-title":"Communication compression for decentralized training","author":"tang","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2932876"},{"key":"ref19","article-title":"Decentralized deep learning with arbitrary communication compression","author":"koloskova","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref18","first-page":"3478","article-title":"Decentralized stochastic optimization and gossip algorithms with compressed communication","author":"koloskova","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3247798"},{"key":"ref50","article-title":"Pruning neural networks at initialization: Why are we missing the mark?","author":"frankle","year":"2021","journal-title":"Int Conf Learn Representations"},{"key":"ref90","first-page":"17 202","article-title":"Sharper convergence guarantees for asynchronous SGD for distributed and federated learning","author":"koloskova","year":"2022","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","article-title":"HeteroFL: Computation and communication efficient federated learning for heterogeneous clients","author":"diao","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref45","article-title":"ELFISH: Resource-aware federated learning on heterogeneous edge devices","author":"xu","year":"2019"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3237752"},{"key":"ref48","article-title":"Neuron-level structured pruning using polarization regularizer","author":"zhuang","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488839"},{"key":"ref42","article-title":"DisPFL: Towards communication-efficient personalized federated learning via decentralized sparse training","author":"dai","year":"2022"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488906"},{"key":"ref41","article-title":"Data-heterogeneity-aware mixing for decentralized learning","author":"dandi","year":"2022"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486403"},{"key":"ref44","article-title":"Model pruning enables efficient federated learning on edge devices","author":"jiang","year":"2019"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3096846"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00954"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796935"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00085"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488817"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3156756"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2984887"},{"key":"ref6","first-page":"5331","article-title":"Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent","author":"lian","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900461"},{"key":"ref40","article-title":"Yes, topology matters in decentralized optimization: Refined convergence and topology learning under heterogeneous data","author":"bars","year":"2022"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488679"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3075291"},{"key":"ref80","article-title":"Learning intrinsic sparse structures within long short-term memory","author":"wen","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref35","first-page":"11422","article-title":"An improved analysis of gradient tracking for decentralized machine learning","author":"koloskova","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref79","first-page":"212","article-title":"Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD","author":"wang","year":"2019","journal-title":"Proc Mach Learn Syst Conf"},{"key":"ref34","article-title":"Decentralized stochastic gradient tracking for non-convex empirical risk minimization","author":"zhang","year":"2019"},{"key":"ref78","article-title":"LEAF: A benchmark for federated settings","author":"caldas","year":"2018"},{"key":"ref37","first-page":"6654","article-title":"Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data","author":"lin","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00302"},{"key":"ref31","first-page":"4848","article-title":": Decentralized training over decentralized data","author":"tang","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref75","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref30","first-page":"344","article-title":"Stochastic gradient push for distributed deep learning","author":"assran","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2016.2524588"},{"key":"ref77","article-title":"Tiny imageNet visual recognition challenge","volume":"7","author":"le","year":"2015","journal-title":"CS 231N"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2875898"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488756"},{"key":"ref1","author":"cisco","year":"2018","journal-title":"Cisco global cloud index Forecast and methodology 2016&#x2013;2021"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS55811.2022.00011"},{"key":"ref38","first-page":"28004","article-title":"Relaysum for decentralized deep learning on heterogeneous data","author":"vogels","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref71","article-title":"Cuda toolkit documentation v10.0","year":"2018"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3136308"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref72","article-title":"cuDNN library developer guide v7.5.0","year":"2018"},{"key":"ref24","first-page":"4087","article-title":"A linearly convergent algorithm for decentralized optimization: Sending less bits for free!","author":"kovalev","year":"2021","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref68","article-title":"FOCUS: Dealing with label quality disparity in federated learning","author":"chen","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17246"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00263"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICC47138.2019.9123209"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3145576"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref20","article-title":"DeepSqueeze: Decentralization meets error-compensated compression","author":"tang","year":"2019"},{"key":"ref64","first-page":"1","article-title":"PuLP: A linear programming toolkit for python","author":"mitchell","year":"2011"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155524"},{"key":"ref22","article-title":"PowerGossip: Practical low-rank communication compression in decentralized deep learning","author":"vogels","year":"2020"},{"key":"ref66","article-title":"FairFed: Enabling group fairness in federated learning","author":"ezzeldin","year":"2021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3026398"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3168969"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118424"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00153"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00040"},{"key":"ref60","first-page":"425","article-title":"Federated learning under arbitrary communication patterns","author":"avdiukhin","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737543"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155268"}],"container-title":["IEEE Transactions on Parallel and Distributed Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/71\/10201356\/10214335.pdf?arnumber=10214335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:31:43Z","timestamp":1695666703000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10214335\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":90,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tpds.2023.3303967","relation":{},"ISSN":["1045-9219","1558-2183","2161-9883"],"issn-type":[{"value":"1045-9219","type":"print"},{"value":"1558-2183","type":"electronic"},{"value":"2161-9883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]}}}