{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T01:50:14Z","timestamp":1775785814125,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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":["2022YFB3104500"],"award-info":[{"award-number":["2022YFB3104500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20177"],"award-info":[{"award-number":["U20A20177"]}],"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":["62272348"],"award-info":[{"award-number":["62272348"]}],"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":["U22B2022"],"award-info":[{"award-number":["U22B2022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Wuhan Science and Technology Joint Project for Building a Strong Transportation Country","award":["2023-2-7"],"award-info":[{"award-number":["2023-2-7"]}]},{"name":"Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy","award":["GML-KF-22-07"],"award-info":[{"award-number":["GML-KF-22-07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Sustain. Comput."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1109\/tsusc.2024.3379440","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T18:35:40Z","timestamp":1710959740000},"page":"848-861","source":"Crossref","is-referenced-by-count":11,"title":["Using Third-Party Auditor to Help Federated Learning: An Efficient Byzantine-Robust Federated Learning"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7749-2718","authenticated-orcid":false,"given":"Zhuangzhuang","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9897-1953","authenticated-orcid":false,"given":"Libing","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2446-7436","authenticated-orcid":false,"given":"Debiao","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-330X","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Geelong, VIC, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1062-8902","authenticated-orcid":false,"given":"Na","family":"Lu","sequence":"additional","affiliation":[{"name":"School of artificial intelligence, Wuhan Technology and Business University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2953-2927","authenticated-orcid":false,"given":"Xuejiang","family":"Wei","sequence":"additional","affiliation":[{"name":"School of artificial intelligence, Wuhan Technology and Business University, Wuhan, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2020.3043758"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2022.3188330"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3110052"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3196404"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3183170"},{"key":"ref6","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2023.3279111"},{"key":"ref8","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018"},{"key":"ref9","first-page":"16070","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref10","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00383"},{"key":"ref12","article-title":"Neurotoxin: Durable backdoors in federated learning","author":"Zhang","year":"2022"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref14","first-page":"1641","article-title":"Justinian\u2019s GAAvernor: Robust distributed learning with gradient aggregation agent","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Pan"},{"key":"ref15","first-page":"1","article-title":"DBA: Distributed backdoor attacks against federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xie"},{"key":"ref16","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref17","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref18","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in Byzantium","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mhamdi"},{"key":"ref19","first-page":"301","article-title":"The limitations of federated learning in sybil settings","volume-title":"Proc. 23rd Int. Symp. Res. Attacks Intrusions Defenses","author":"Fung"},{"key":"ref20","first-page":"10495","article-title":"Zeno : Robust fully asynchronous SGD","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3167434"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3135422"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20903"},{"key":"ref24","first-page":"43158","article-title":"LeadFL: Client self-defense against model poisoning in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599346"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450066"},{"key":"ref27","first-page":"6893","article-title":"Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS51616.2021.00086"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3221899"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102631"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2021.11.006"},{"key":"ref33","first-page":"6365","article-title":"FedVal: Different good or different bad in federated learning","volume-title":"Proc. 32nd USENIX Secur. Symp.","author":"Valadi"},{"key":"ref34","article-title":"Byzantine-robust federated machine learning through adaptive model averaging","author":"Mu\u00f1oz-Gonz\u00e1lez","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"ref37","first-page":"840","article-title":"Sageflow: Robust federated learning against both stragglers and adversaries","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Park"},{"key":"ref38","first-page":"7074","article-title":"Defending against saddle point attack in Byzantine-robust distributed learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref39","first-page":"15111","article-title":"Distributionally robust federated averaging","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Deng"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.006.2200651"},{"key":"ref41","first-page":"508","article-title":"AUROR: Defending against poisoning attacks in collaborative deep learning systems","volume-title":"Proc. 32nd Annu. Conf. Comput. Secur. Appl.","author":"Shen"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref43","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang"},{"key":"ref44","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019"},{"key":"ref45","first-page":"1415","article-title":"FLAME: Taming backdoors in federated learning","volume-title":"Proc. 31st USENIX Secur. Symp.","author":"Nguyen"},{"key":"ref46","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Sustainable Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7274860\/10791304\/10475552.pdf?arnumber=10475552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T20:57:04Z","timestamp":1733950624000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10475552\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":47,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tsusc.2024.3379440","relation":{},"ISSN":["2377-3782","2377-3790"],"issn-type":[{"value":"2377-3782","type":"electronic"},{"value":"2377-3790","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}