{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:56:21Z","timestamp":1782402981948,"version":"3.54.5"},"reference-count":90,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1453073"],"award-info":[{"award-number":["CCF-1453073"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1907658"],"award-info":[{"award-number":["CCF-1907658"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-1940074"],"award-info":[{"award-number":["OAC-1940074"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF2110301"],"award-info":[{"award-number":["W911NF2110301"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Signal and Inf. Process. over Networks"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tsipn.2022.3188456","type":"journal-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T19:26:12Z","timestamp":1657049172000},"page":"610-626","source":"Crossref","is-referenced-by-count":71,"title":["BRIDGE: Byzantine-Resilient Decentralized Gradient Descent"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4318-1078","authenticated-orcid":false,"given":"Cheng","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiong","family":"Yang","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4406-5263","authenticated-orcid":false,"given":"Waheed U.","family":"Bajwa","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"ref3","first-page":"3","article-title":"Supervised machine learning: A review of classification techniques","volume":"160","author":"Kotsiantis","year":"2007","journal-title":"Emerg. Artif. Intell. Appl. Comput. Eng."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref5","volume-title":"Foundations of Machine Learning","author":"Mohri","year":"2018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1201\/9781351051507"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2973345"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3021381"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2006.1657817"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1561\/2200000051"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2817461"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3196503"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39878-3_19"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2933057.2933105"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/357172.357176"},{"key":"ref17","article-title":"Best-case complexity of asynchronous Byzantine consensus","author":"Dutta","year":"2005"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/EDCC.2012.32"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5555\/2685048.2685095"},{"key":"ref20","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Konen","year":"2016"},{"key":"ref21","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst","author":"Blanchard","year":"2017"},{"key":"ref22","first-page":"903","article-title":"DRACO: Byzantine-resilient distributed training via redundant gradients","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Chen","year":"2018"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461691"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3322205.3311083"},{"key":"ref25","first-page":"7074","article-title":"Defending against saddle point attack in Byzantine-robust distributed learning","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Yin","year":"2019"},{"key":"ref26","first-page":"1145","article-title":"Asynchronous Byzantine machine learning (the case of SGD)","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Damaskinos","year":"2018"},{"key":"ref27","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in Byzantium","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Mhamdi","year":"2018"},{"key":"ref28","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Yin","year":"2018"},{"key":"ref29","first-page":"4618","article-title":"Byzantine stochastic gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alistarh","year":"2018"},{"key":"ref30","first-page":"97","article-title":"Zeno: Byzantine-suspicious stochastic gradient descent","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Xie","year":"2019"},{"key":"ref31","article-title":"Generalized Byzantine-tolerant SGD","volume-title":"arXiv:1802.10116","author":"Xie","year":"2018"},{"key":"ref32","article-title":"Phocas: Dimensional Byzantine-resilient stochastic gradient descent","volume-title":"arXiv:1805.09682","author":"Xie"},{"key":"ref33","first-page":"21616","article-title":"Distributed training with heterogeneous data: Bridging median- and mean-based algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2020"},{"key":"ref34","article-title":"DETOX: A redundancy-based framework for faster and more robust gradient aggregation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Rajput","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761674"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683121"},{"key":"ref38","article-title":"Robust federated learning in a heterogeneous environment","author":"Ghosh","year":"2019"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2020.3035868"},{"key":"ref40","article-title":"SGD: Decentralized Byzantine resilience","volume":"abs\/1905.03853","author":"Mhamdi","year":"2019"},{"key":"ref41","first-page":"10495","article-title":"Zeno: Robust fully asynchronous SGD","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Xie","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS51746.2020.00015"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.2009515"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-010-9737-7"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2364096"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-020-01487-0"},{"key":"ref47","first-page":"1663","article-title":"Consensus-based distributed support vector machines","volume":"11","author":"Forero","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2254478"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2304432"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2016.2613678"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2617829"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2013.130413"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-45249-9_2"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2018.2836919"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472116"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2018.06.024"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-018-9813-7"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2019.2951686"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2019.2928176"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.23919\/ACC45564.2020.9147396"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108020"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3116976"},{"key":"ref63","first-page":"34","article-title":"Collaborative learning in the jungle (decentralized, byzantine, heterogeneous, asynchronous and nonconvex learning)","author":"El-Mhamdi","year":"2021"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2016.2524588"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2818081"},{"key":"ref66","first-page":"9217","article-title":"Improving the sample and communication complexity for decentralized non-convex optimization: Joint gradient estimation and tracking","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Sun","year":"2020"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1137\/20m1361158"},{"key":"ref68","article-title":"Byzantine-robust decentralized learning via self-centered clipping","author":"He","year":"2022"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref70","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-1841-6"},{"key":"ref72","article-title":"When are nonconvex problems not scary","author":"Sun","year":"2016"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1561\/2200000058"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3007967"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2016.7852249"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1137\/0721040"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/s10957-018-1272-y"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852239"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2011.2161027"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2016.7526806"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CDC40024.2019.9029245"},{"key":"ref82","first-page":"2478","article-title":"Byzantine-resilient high-dimensional SGD with local iterations on heterogeneous data","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Data","year":"2021"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2_594"},{"key":"ref84","article-title":"Fault-tolerant distributed optimization (Part IV): Constrained optimization with arbitrary directed networks","volume-title":"arXiv:1511.01821","author":"Su","year":"2015"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9"},{"key":"ref86","article-title":"BRIDGE: Byzantine-resilient decentralized gradient descent","author":"Yang","year":"2019","journal-title":"arXiv:1908.08098v1"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOS1637"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2021.1126"},{"key":"ref89","article-title":"Uniform convergence of gradients for non-convex learning and optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Foster","year":"2018"},{"key":"ref90","article-title":"Classifying CIFAR-10 images using unsupervised feature & ensemble learning","author":"Le","year":"2016"}],"container-title":["IEEE Transactions on Signal and Information Processing over Networks"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6884276\/9666472\/9815556-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6884276\/9666472\/09815556.pdf?arnumber=9815556","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T05:31:04Z","timestamp":1706765464000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9815556\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":90,"URL":"https:\/\/doi.org\/10.1109\/tsipn.2022.3188456","relation":{},"ISSN":["2373-776X","2373-7778"],"issn-type":[{"value":"2373-776X","type":"electronic"},{"value":"2373-7778","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}