{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:03:22Z","timestamp":1773997402846,"version":"3.50.1"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"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":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/globecom59602.2025.11432607","type":"proceedings-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:04:01Z","timestamp":1773950641000},"page":"4867-4872","source":"Crossref","is-referenced-by-count":0,"title":["Embedding Byzantine Fault Tolerance into Federated Learning via Consistency Scoring"],"prefix":"10.1109","author":[{"given":"Youngjoon","family":"Lee","sequence":"first","affiliation":[{"name":"KAIST,School of Electrical Engineering,South Korea"}]},{"given":"Jinu","family":"Gong","sequence":"additional","affiliation":[{"name":"Hansung University,Department of Applied AI,South Korea"}]},{"given":"Joonhyuk","family":"Kang","sequence":"additional","affiliation":[{"name":"KAIST,School of Electrical Engineering,South Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.001.2400150"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM54140.2023.10437695"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104130"},{"key":"ref4","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. AISTAT","author":"McMahan"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-05185-1_51"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP49359.2023.10222453"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3533708"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3203581"},{"key":"ref11","article-title":"How to backdoor federated learning","volume-title":"Proc. AISTAT, Virutal Event","author":"Bagdasaryan"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref13","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. MLSys","author":"Li"},{"key":"ref14","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. ICLR, Virtual Event","author":"Acar"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467254"},{"key":"ref16","article-title":"Generalized federated learning via sharpness aware minimization","volume-title":"Proc. ICML","author":"Qu"},{"key":"ref17","article-title":"Sharpness-aware minimization for efficiently improving generalization","volume-title":"Proc. ICLR","author":"Foret"},{"key":"ref18","article-title":"Fedspeed: Larger local interval, less communication round, and higher generalization accuracy","volume-title":"Proc. ICLR","author":"Sun"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI56570.2024.10635545"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105474"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref23","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. NeurIPS","author":"Blanchard"},{"key":"ref24","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. ICML","author":"Yin"},{"key":"ref25","article-title":"Local model poisoning attacks to byzantine-robust federated learning","volume-title":"Proc. USENIX Security Symposium, Virutal Event","author":"Fang"}],"event":{"name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","location":"Taipei, Taiwan","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,12]]}},"container-title":["GLOBECOM 2025 - 2025 IEEE Global Communications Conference"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11431620\/11431622\/11432607.pdf?arnumber=11432607","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T05:32:50Z","timestamp":1773984770000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11432607\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/globecom59602.2025.11432607","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}