{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:56:22Z","timestamp":1782402982834,"version":"3.54.5"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["61973324"],"award-info":[{"award-number":["61973324"]}],"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":["12126610"],"award-info":[{"award-number":["12126610"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021B1515020094"],"award-info":[{"award-number":["2021B1515020094"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Key Laboratory of Computational Science at Sun Yat-Sen University","award":["2020B1212060032"],"award-info":[{"award-number":["2020B1212060032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tsp.2023.3300629","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T17:46:41Z","timestamp":1691084801000},"page":"3179-3195","source":"Crossref","is-referenced-by-count":50,"title":["Byzantine-Resilient Decentralized Stochastic Optimization With Robust Aggregation Rules"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6724-6134","authenticated-orcid":false,"given":"Zhaoxian","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering and Guangdong Provincial Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-1439","authenticated-orcid":false,"given":"Tianyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4222-5964","authenticated-orcid":false,"given":"Qing","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering and Guangdong Provincial Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Secure distributed training at scale","author":"gorbunov","year":"0"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2009.25"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/670"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747253"},{"key":"ref15","article-title":"Byzantine resilient non-convex SVRG with distributed batch gradient computations","author":"khanduri","year":"0"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysconle.2004.02.022"},{"key":"ref31","article-title":"Byzantine-robust decentralized learning via self-centered clipping","author":"he","year":"0"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108020"},{"key":"ref11","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in Byzantium","author":"mhamdi","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3116976"},{"key":"ref10","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.23919\/ACC45564.2020.9147396"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref1","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"kone?n?","year":"0"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9518248"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2364096"},{"key":"ref16","first-page":"5311","article-title":"Learning from history for Byzantine robust optimization","author":"karimireddy","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref38","article-title":"Finite-time error bounds for distributed linear stochastic approximation","author":"lin","year":"0"},{"key":"ref19","first-page":"1","article-title":"Byzantine-robust learning on heterogeneous datasets via bucketing","author":"karimireddy","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref24","first-page":"5336","article-title":"Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent","author":"lian","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","first-page":"8635","article-title":"A little is enough: Circumventing defenses for distributed learning","volume":"32","author":"baruch","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.2009515"},{"key":"ref45","first-page":"261","article-title":"Fall of empires: Breaking Byzantine&#x2013;tolerant SGD by inner product manipulation","author":"xie","year":"0","journal-title":"Proc 35th Uncertainty Artif Intell"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.3008139"},{"key":"ref25","article-title":"Fault-tolerant distributed optimization (part IV): Constrained optimization with arbitrary directed networks","author":"su","year":"0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.10.120"},{"key":"ref42","first-page":"5381","article-title":"A unified theory of decentralized SGD with changing topology and local updates","author":"koloskova","year":"0","journal-title":"Int Conf Mach Learn"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/130943170"},{"key":"ref22","article-title":"Learning to detect malicious clients for robust federated learning","author":"li","year":"0"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2817461"},{"key":"ref21","article-title":"Zeno: Byzantine-suspicious stochastic gradient descent","author":"xie","year":"0"},{"key":"ref43","first-page":"13975","article-title":"Exponential graph is provably efficient for decentralized deep training","volume":"34","author":"ying","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2019.2928176"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3188456"},{"key":"ref29","first-page":"25044","article-title":"Collaborative learning in the jungle (decentralized, Byzantine, heterogeneous, asynchronous and nonconvex learning)","author":"el-mhamdi","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref8","article-title":"Generalized Byzantine-tolerant SGD","author":"xie","year":"0"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3219617.3219655"},{"key":"ref9","article-title":"Phocas: Dimensional Byzantine-resilient stochastic gradient descent","author":"xie","year":"0"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/357172.357176"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3202887"},{"key":"ref6","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","author":"yin","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2973345"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.2972824"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/10040758\/10208131.pdf?arnumber=10208131","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T19:39:46Z","timestamp":1696880386000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10208131\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/tsp.2023.3300629","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"value":"1053-587X","type":"print"},{"value":"1941-0476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}