{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T17:32:03Z","timestamp":1777829523632,"version":"3.51.4"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2105004"],"award-info":[{"award-number":["CNS-2105004"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2024.3516567","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T20:08:54Z","timestamp":1740082134000},"page":"3758-3771","source":"Crossref","is-referenced-by-count":7,"title":["Can We Trust the Similarity Measurement in Federated Learning?"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-0332","authenticated-orcid":false,"given":"Zhilin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Purdue University, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8847-8345","authenticated-orcid":false,"given":"Qin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xukai","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Indiana University Indianapolis, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7935-886X","authenticated-orcid":false,"given":"Pengfei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5912-4647","authenticated-orcid":false,"given":"Xiuzhen","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, Shandong, China"}],"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. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3088655"},{"key":"ref3","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp. (USENIX Secur.)","author":"Fang"},{"key":"ref4","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref5","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":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/fi13030073"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC51071.2022.9771619"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00057"},{"key":"ref10","first-page":"8635","article-title":"A little is enough: Circumventing defenses for distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Baruch"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"ref13","article-title":"Challenges and approaches for mitigating Byzantine attacks in federated learning","author":"Shi","year":"2021","journal-title":"arXiv:2112.14468"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3196274"},{"key":"ref15","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3402113"},{"key":"ref17","first-page":"7587","article-title":"SparseFed: Mitigating model poisoning attacks in federated learning with sparsification","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Panda"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CISS53076.2022.9751167"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3353317"},{"key":"ref21","article-title":"New metrics to evaluate the performance and fairness of personalized federated learning","author":"Divi","year":"2021","journal-title":"arXiv:2107.13173"},{"key":"ref22","first-page":"3407","article-title":"Clustered sampling: Low-variance and improved representativity for clients selection in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fraboni"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467681"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref26","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":"ref27","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019","journal-title":"arXiv:1911.07963"},{"key":"ref28","first-page":"1415","article-title":"FLAME: Taming backdoors in federated learning","volume-title":"Proc. 31st USENIX Secur. Symp. (USENIX Secur.)","author":"Nguyen"},{"key":"ref29","article-title":"Secure and fault tolerant decentralized learning","author":"Prakash","year":"2020","journal-title":"arXiv:2010.07541"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3169918"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/DAPPS57946.2023.00012"},{"key":"ref33","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in Byzantium","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Mhamdi"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110178"},{"key":"ref35","article-title":"G2uardFL: Safeguarding federated learning against backdoor attacks through attributed client graph clustering","author":"Yu","year":"2023","journal-title":"arXiv:2306.04984"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3488932.3517395"},{"key":"ref37","article-title":"Avoid adversarial adaption in federated learning by multi-metric investigations","author":"Krau\u00df","year":"2023","journal-title":"arXiv:2306.03600"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS54860.2022.00120"},{"key":"ref39","article-title":"Untargeted poisoning attack detection in federated learning via behavior attestation","author":"Mallah","year":"2021","journal-title":"arXiv:2101.10904"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3567991"},{"key":"ref41","first-page":"10495","article-title":"Zeno++: Robust fully asynchronous SGD","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3315125"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3314748"},{"key":"ref44","article-title":"Kick bad guys out! Conditionally activated anomaly detection in federated learning with zero-knowledge proof verification","author":"Han","year":"2023","journal-title":"arXiv:2310.04055"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612474"},{"key":"ref46","volume-title":"MNIST Handwritten Digit Database","author":"LeCun","year":"2010"},{"key":"ref47","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref48","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/10206\/10810755\/10896961-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/10896961.pdf?arnumber=10896961","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T04:25:35Z","timestamp":1744345535000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10896961\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/tifs.2024.3516567","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}