{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:12:04Z","timestamp":1778166724745,"version":"3.51.4"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1109\/tii.2021.3128164","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T22:34:48Z","timestamp":1637015688000},"page":"1165-1175","source":"Crossref","is-referenced-by-count":75,"title":["Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-3127","authenticated-orcid":false,"given":"Shenghui","family":"Li","sequence":"first","affiliation":[{"name":"Department of Information Technology, Uppsala University, Uppsala, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-8731","authenticated-orcid":false,"given":"Edith","family":"Ngai","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2586-8573","authenticated-orcid":false,"given":"Thiemo","family":"Voigt","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Uppsala University, Uppsala, Sweden"}]}],"member":"263","reference":[{"key":"ref39","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","author":"bhagoji","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref38","first-page":"261","article-title":"Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation","author":"xie","year":"0","journal-title":"Proc Conf Uncertainty of Artificial Intelligence"},{"key":"ref33","first-page":"2938","article-title":"How to backdoor federated learning","author":"bagdasaryan","year":"0","journal-title":"Proc 23rd Int Conf Artif Intell Statist"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500698"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM42002.2020.9322464"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3010798"},{"key":"ref37","article-title":"Threats to federated learning: A survey","author":"lyu","year":"2020"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347978"},{"key":"ref35","first-page":"3488","article-title":"Robust learning from untrusted sources","author":"konstantinov","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000372"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3043458"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/2897518.2897647"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3032165"},{"key":"ref12","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","author":"fang","year":"0","journal-title":"Proc 29th USENIX Conf Secur Symp"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3024763"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.2979149"},{"key":"ref16","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"0","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.301.2000478"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3219617.3219655"},{"key":"ref19","article-title":"Generalized byzantine-tolerant SGD","author":"xie","year":"2018"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3064351"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700374"},{"key":"ref27","article-title":"Federated learning for malware detection in IoT devices","author":"rey","year":"2021"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/s21041470"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3012216"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops50388.2021.9473515"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2017.10.002"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2945367"},{"key":"ref7","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"0","journal-title":"Proc 20th Int Conf Artif Intell Statist"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2011.5940562"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2013.124"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3016025"},{"key":"ref20","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","author":"yin","year":"0","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2020.2993259"},{"key":"ref21","article-title":"Robust aggregation for federated learning","author":"pillutla","year":"2019"},{"key":"ref24","article-title":"Fed : A unified approach to robust personalized federated learning","author":"yu","year":"2021"},{"key":"ref42","article-title":"LEAF: A benchmark for federated settings","author":"caldas","year":"2019"},{"key":"ref23","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","author":"li","year":"0","journal-title":"Proc 38th Int Conf Mach Learn"},{"key":"ref41","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","author":"li","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3023430"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataSE50710.2020.00020"},{"key":"ref43","first-page":"1","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"0","journal-title":"Proc Mach Learn Syst"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/9989328\/09614992.pdf?arnumber=9614992","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T19:21:34Z","timestamp":1673896894000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9614992\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":43,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tii.2021.3128164","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2]]}}}