{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T12:59:28Z","timestamp":1782997168589,"version":"3.54.5"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["61971128"],"award-info":[{"award-number":["61971128"]}],"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":["U1936201"],"award-info":[{"award-number":["U1936201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB1804901"],"award-info":[{"award-number":["2020YFB1804901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242022k30002"],"award-info":[{"award-number":["2242022k30002"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Commun."],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1109\/tcomm.2022.3200111","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T19:31:52Z","timestamp":1660937512000},"page":"6563-6578","source":"Crossref","is-referenced-by-count":13,"title":["Backdoor Federated Learning-Based mmWave Beam Selection"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3096-1286","authenticated-orcid":false,"given":"Zhengming","family":"Zhang","sequence":"first","affiliation":[{"name":"National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, School of Information Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6119-613X","authenticated-orcid":false,"given":"Ruming","family":"Yang","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, School of Information Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1297-6951","authenticated-orcid":false,"given":"Xiangyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, School of Information Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4689-7226","authenticated-orcid":false,"given":"Chunguo","family":"Li","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, School of Information Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3616-4616","authenticated-orcid":false,"given":"Yongming","family":"Huang","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, School of Information Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1474-1806","authenticated-orcid":false,"given":"Luxi","family":"Yang","sequence":"additional","affiliation":[{"name":"National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, School of Information Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3104219"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3038787"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2009.091009"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/VETECF.2009.5379063"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3003670"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2018.8503086"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.clsr.2018.05.012"},{"key":"ref8","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"arXiv:1610.05492","author":"Kone\u010dn\u00fd","year":"2016"},{"key":"ref9","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2020.3019312"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2021.3099136"},{"key":"ref12","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref13","first-page":"303","article-title":"Wireless jamming attacks under dynamic traffic uncertainty","volume-title":"Proc. 8th Int. Symp. Modeling Optim. Mobile, Ad Hoc, Wireless Netw.","author":"Sagduyu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2018.2825178"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2011.5751298"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2011.041110.00022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2021.3120290"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps52748.2021.9682097"},{"key":"ref19","article-title":"Explaining and harnessing adversarial examples","volume-title":"arXiv:1412.6572","author":"Goodfellow","year":"2014"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3154503"},{"key":"ref22","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Blanchard"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2795385"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2019.2899571"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3076613"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3090331"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3003744"},{"key":"ref28","first-page":"16070","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Wang"},{"key":"ref29","first-page":"1","article-title":"Are adversarial examples inevitable?","volume-title":"Proc. Int. Conf. Learn. Represent. (Poster)","author":"Shafahi"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3021407"},{"key":"ref31","first-page":"2484","article-title":"Towards understanding the dynamics of the first-order adversaries","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Deng"},{"key":"ref32","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Wang"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICC47138.2019.9123209"},{"key":"ref35","first-page":"1","article-title":"Adaptive federated optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Reddi"},{"key":"ref36","first-page":"2021","article-title":"On the convergence of federated optimization in heterogeneous networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Sahu"},{"key":"ref37","article-title":"Can you really backdoor federated learning?","volume-title":"arXiv:1911.07963","author":"Sun","year":"2019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(93)90009-P"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.131173198"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2018.2870360"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3061212"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCW.2015.7247314"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2017.2773532"},{"key":"ref44","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"arXiv:1409.1556","author":"Simonyan","year":"2014"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2995699"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2014.2363831"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2021.3053967"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/BF01086099"},{"key":"ref50","first-page":"20596","article-title":"Deep learning on a data diet: Finding important examples early in training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Paul"}],"container-title":["IEEE Transactions on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/26\/9920689\/09862973.pdf?arnumber=9862973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:48:50Z","timestamp":1706788130000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9862973\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":50,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tcomm.2022.3200111","relation":{},"ISSN":["0090-6778","1558-0857"],"issn-type":[{"value":"0090-6778","type":"print"},{"value":"1558-0857","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10]]}}}