{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:14:29Z","timestamp":1774023269912,"version":"3.50.1"},"reference-count":45,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1109\/iccworkshops50388.2021.9473694","type":"proceedings-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T20:08:20Z","timestamp":1625861300000},"page":"1-6","source":"Crossref","is-referenced-by-count":21,"title":["Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation"],"prefix":"10.1109","author":[{"given":"Shaoming","family":"Huang","sequence":"first","affiliation":[{"name":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[{"name":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University,Shanghai Key Lab. of Trustworthy Computing, School of Software Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanming","family":"Shi","sequence":"additional","affiliation":[{"name":"ShanghaiTech University,School of Information Science and Technology,Shanghai,China,201210"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"355","article-title":"Sur le point pour lequel la somme des distances de n points donnés est minimum","volume":"43","author":"weiszfeld","year":"1937","journal-title":"Tohoku Math J"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000045"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1109\/TIT.2007.904785","article-title":"Computation over multiple-access channels","volume":"53","author":"nazer","year":"2007","journal-title":"IEEE Trans Inf Theory"},{"key":"ref31","article-title":"Robust aggregation for federated learning","author":"pillutla","year":"2019"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2970338"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3015489"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2272921"},{"key":"ref34","article-title":"Federated learning via intelligent reflecting surface","author":"wang","year":"2020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1900025"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1137\/100818327"},{"key":"ref11","article-title":"Towards federated learning at scale: System design","author":"bonawitz","year":"2019","journal-title":"Conference on System and Machine Learning (SysML)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1504\/IJSN.2015.071829"},{"key":"ref13","first-page":"601","article-title":"Stealing machine learning models via prediction apis","author":"tram\u00e8r","year":"2016","journal-title":"Proc Usenix Secur Symp"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036948"},{"key":"ref16","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume":"80","author":"yin","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref17","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","author":"wang","year":"2020","journal-title":"NeurIPS"},{"key":"ref18","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","author":"bhagoji","year":"2019","journal-title":"Proc 36th Intl Conf Mach Learn"},{"key":"ref19","first-page":"2938","article-title":"How to backdoor federated learning","author":"bagdasaryan","year":"2020","journal-title":"Proc Int Conf Artif Intell Stat (AISTATS)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3150\/14-BEJ645"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3007787"},{"key":"ref27","first-page":"119","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"2017","journal-title":"Proc Neural Inf Process Syst (NeuralPS)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900461"},{"key":"ref6","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"mcmahan","year":"2017","journal-title":"Proc Int Conf Artif Intell Stat (AISTATS)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3154503"},{"key":"ref5","first-page":"31","article-title":"Communication-efficient edge ai inference over wireless networks","volume":"18","author":"yang","year":"2020","journal-title":"ZTE Commun"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2961673"},{"key":"ref7","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019"},{"key":"ref2","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2019.1900271"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2019.2921755"},{"key":"ref45","article-title":"Over-the-air computing for 6G–turning air into a computer","author":"zhu","year":"2020"},{"key":"ref22","first-page":"903","article-title":"DRACO: Byzantine-resilient distributed training via redundant gradients","author":"chen","year":"2018","journal-title":"Proc 35th Intl Conf Mach Learn"},{"key":"ref21","article-title":"Aggregathor: Byzantine machine learning via robust gradient aggregation","author":"damaskinos","year":"2019","journal-title":"Conference on System and Machine Learning (SysML)"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/APUSNCURSINRSM.2018.8608340"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref41","first-page":"5370","article-title":"Efficient on-device models using neural projections","author":"ravi","year":"2019","journal-title":"Proc 36th Intl Conf Mach Learn"},{"key":"ref23","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in byzantium","volume":"80","author":"mhamdi","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2981904"},{"key":"ref26","article-title":"Byzantine-resilient secure federated learning","author":"so","year":"2020","journal-title":"IEEE J Sel Areas Commun"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2989580"},{"key":"ref25","first-page":"4613","article-title":"Byzantine stochastic gradient descent","volume":"31","author":"alistarh","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"}],"event":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","location":"Montreal, QC, Canada","start":{"date-parts":[[2021,6,14]]},"end":{"date-parts":[[2021,6,23]]}},"container-title":["2021 IEEE International Conference on Communications Workshops (ICC Workshops)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9473476\/9473480\/09473694.pdf?arnumber=9473694","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:34:41Z","timestamp":1659483281000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9473694\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/iccworkshops50388.2021.9473694","relation":{},"subject":[],"published":{"date-parts":[[2021,6]]}}}