{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T05:47:44Z","timestamp":1767332864965,"version":"3.48.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Union's Horizon Europe research and innovation programme","award":["101178099"],"award-info":[{"award-number":["101178099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tifs.2025.3643162","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T18:44:27Z","timestamp":1765478667000},"page":"404-416","source":"Crossref","is-referenced-by-count":0,"title":["FLgym: Toward Robust and Byzantine-Resilient Federated Learning"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6713-0053","authenticated-orcid":false,"given":"Ke","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Computing Science, University of Glasgow, Glasgow, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2298-7195","authenticated-orcid":false,"given":"Qiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing Science, University of Glasgow, Glasgow, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1517-6757","authenticated-orcid":false,"given":"Christos","family":"Anagnostopoulos","sequence":"additional","affiliation":[{"name":"School of Computing Science, University of Glasgow, Glasgow, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_17"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-02155-w"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3407792"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3239542"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488817"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299573"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352628"},{"key":"ref9","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref10","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref12","first-page":"8632","article-title":"A little is enough: Circumventing defenses for distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Baruch"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3420126"},{"key":"ref14","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":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3402113"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539231"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671906"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3266347"},{"key":"ref20","first-page":"261","article-title":"Fall of empires: Breaking Byzantine-tolerant SGD by inner product manipulation","volume-title":"Proc. 35th Uncertainty Artif. Intell. Conf.","author":"Xie"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1051\/sands\/2023006"},{"key":"ref22","first-page":"5311","article-title":"Learning from history for Byzantine robust optimization","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"139","author":"Karimireddy"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3354736"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3352415"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3280032"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2025.3594175"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3555193"},{"key":"ref28","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref29","first-page":"1","article-title":"Secure federated learning against model poisoning attacks via client filtering","volume-title":"Proc. ICLR Workshop","author":"Yaldiz"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"volume-title":"MNIST Handwritten Digit Database","year":"2010","author":"LeCun","key":"ref31"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCSW63273.2025.00021"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3445637"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref34"},{"key":"ref35","first-page":"1","article-title":"On bridging generic and personalized federated learning for image classification","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref36","first-page":"4587","article-title":"DisPFL: Towards communication-efficient personalized federated learning via decentralized sparse training","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Dai"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3554980"},{"key":"ref38","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/11313711\/11298320.pdf?arnumber=11298320","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T05:44:22Z","timestamp":1767332662000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11298320\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3643162","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"type":"print","value":"1556-6013"},{"type":"electronic","value":"1556-6021"}],"subject":[],"published":{"date-parts":[[2026]]}}}