{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:21:46Z","timestamp":1777486906693,"version":"3.51.4"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"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":["2020YFB1005900"],"award-info":[{"award-number":["2020YFB1005900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122042"],"award-info":[{"award-number":["62122042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput."],"published-print":{"date-parts":[[2023,9,1]]},"DOI":"10.1109\/tc.2023.3257510","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T17:34:31Z","timestamp":1678901671000},"page":"2600-2614","source":"Crossref","is-referenced-by-count":32,"title":["Byzantine-Resilient Federated Learning at Edge"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6750-1190","authenticated-orcid":false,"given":"Youming","family":"Tao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7304-9064","authenticated-orcid":false,"given":"Sijia","family":"Cui","sequence":"additional","affiliation":[{"name":"Institute of Automation, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlu","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haofei","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-5981","authenticated-orcid":false,"given":"Dongxiao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8207-6740","authenticated-orcid":false,"given":"Weifa","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5912-4647","authenticated-orcid":false,"given":"Xiuzhen","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2020.3003645"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3007787"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107659"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3063889"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9518248"},{"key":"ref10","first-page":"1","article-title":"Distributed momentum for byzantine-resilient stochastic gradient descent","author":"el mhamdi","year":"2021","journal-title":"Proc 9th Int Conf Learn Representations"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1002\/9780470181218","author":"biswas","year":"2007","journal-title":"Statistical Advances in the Biomedical Sciences Clinical Trials Epidemiology Survival Analysis and Bioinformatics"},{"key":"ref16","author":"woolson","year":"2011","journal-title":"Statistical Methods for the Analysis of Biomedical Data"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/B978-044450896-6.50004-2"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16877-7"},{"key":"ref50","first-page":"2478","article-title":"Byzantine-resilient high-dimensional SGD with local iterations on heterogeneous data","author":"data","year":"2021","journal-title":"Proc 38th Int Conf Mach Learn"},{"key":"ref46","first-page":"1306","article-title":"Gradient sparsification for communication-efficient distributed optimization","author":"wangni","year":"2018","journal-title":"Proc 32nd Conf Neural Inf Process Syst"},{"key":"ref45","article-title":"UCI machine learning repository","author":"dua","year":"2017"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3322205.3311083"},{"key":"ref47","first-page":"1596","article-title":"Sever: A Robust Meta-Algorithm for Stochastic Optimization","author":"diakonikolas","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref42","first-page":"4452","article-title":"ATOMO: Communication-efficient learning via atomic sparsification","author":"wang","year":"2018","journal-title":"Proc 32nd Conf Neural Inf Process Syst"},{"key":"ref41","article-title":"Sparsified SGD with memory","author":"stich","year":"2018","journal-title":"Proc 32nd Conf Neural Inf Process Syst"},{"key":"ref44","first-page":"3252","article-title":"Error feedback fixes signsgd and other gradient compression schemes","author":"karimireddy","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref43","first-page":"1709","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","author":"alistarh","year":"2017","journal-title":"Proc 31st Conf Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS51746.2020.00015"},{"key":"ref8","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","author":"yin","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2015.2495213"},{"key":"ref9","first-page":"6246","article-title":"Byzantine machine learning made easy by resilient averaging of momentums","author":"farhadkhani","year":"2022","journal-title":"Proc 39th Int Conf Mach Learn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3072033"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2011.221"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/357172.357176"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3257510"},{"key":"ref35","first-page":"10081","article-title":"On differentially private stochastic convex optimization with heavy-tailed data","author":"wang","year":"2020","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref34","first-page":"1","article-title":"signSGD with majority vote is communication efficient and fault tolerant","author":"bernstein","year":"2019","journal-title":"Proc 7th Int Conf Learn Representations"},{"key":"ref37","first-page":"10633","article-title":"Improved rates for differentially private stochastic convex optimization with heavy-tailed data","author":"kamath","year":"2022","journal-title":"Proc 39th Int Conf Mach Learn"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3517804.3524144"},{"key":"ref31","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"2017","journal-title":"Proc 31st Conf Neural Inf Process Syst"},{"key":"ref30","article-title":"Stochastic-sign SGD for federated learning with theoretical guarantees","author":"jin","year":"2020"},{"key":"ref33","first-page":"5827","article-title":"A tail-index analysis of stochastic gradient noise in deep neural networks","author":"simsekli","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref32","first-page":"3518","article-title":"The hidden vulnerability of distributed learning in byzantium","author":"guerraoui","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3099723"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Statist"},{"key":"ref39","first-page":"86","author":"tikhomirov","year":"1993","journal-title":"Entropy and -Capacity of Sets in Functional Spaces"},{"key":"ref38","article-title":"Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression","author":"catoni","year":"2017"},{"key":"ref24","first-page":"5311","article-title":"Learning from history for byzantine robust optimization","author":"karimireddy","year":"2021","journal-title":"Proc 38th Int Conf Mach Learn"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3105076"},{"key":"ref26","first-page":"1830","article-title":"Outlier robust mean estimation with subgaussian rates via stability","author":"diakonikolas","year":"2020","journal-title":"Proc 34th Conf Neural Inf Process Syst"},{"key":"ref25","first-page":"6065","article-title":"Quantum entropy scoring for fast robust mean estimation and improved outlier detection","author":"dong","year":"2019","journal-title":"Proc 32nd Conf Neural Inf Process Syst"},{"key":"ref20","first-page":"703","article-title":"Robust descent using smoothed multiplicative noise","author":"holland","year":"2019","journal-title":"Proc 22nd Int Conf Artif Intell Statist"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3154503"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05802-5"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref27","first-page":"1558","article-title":"Robust mean estimation on highly incomplete data with arbitrary outliers","author":"hu","year":"2021","journal-title":"Proc 24th Int Conf Artif Intell Statist"},{"key":"ref29","first-page":"559","article-title":"SIGNSGD: Compressed optimisation for non-convex problems","author":"bernstein","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Computers"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/12\/10213258\/10070815.pdf?arnumber=10070815","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T17:52:11Z","timestamp":1693245131000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10070815\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":50,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tc.2023.3257510","relation":{},"ISSN":["0018-9340","1557-9956","2326-3814"],"issn-type":[{"value":"0018-9340","type":"print"},{"value":"1557-9956","type":"electronic"},{"value":"2326-3814","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]}}}