{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T12:21:32Z","timestamp":1774182092464,"version":"3.50.1"},"reference-count":33,"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":"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFE0209800"],"award-info":[{"award-number":["2023YFE0209800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376210"],"award-info":[{"award-number":["62376210"]}],"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":["62161160337"],"award-info":[{"award-number":["62161160337"]}],"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":["62132011"],"award-info":[{"award-number":["62132011"]}],"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":["U21B2018"],"award-info":[{"award-number":["U21B2018"]}],"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":["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":["U20B2049"],"award-info":[{"award-number":["U20B2049"]}],"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":["62206217"],"award-info":[{"award-number":["62206217"]}],"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":["62006181"],"award-info":[{"award-number":["62006181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Province Key Industry Innovation Program","award":["2023-ZDLGY-38"],"award-info":[{"award-number":["2023-ZDLGY-38"]}]},{"name":"Shaanxi Province Key Industry Innovation Program","award":["2021ZDLGY01-02"],"award-info":[{"award-number":["2021ZDLGY01-02"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M722530"],"award-info":[{"award-number":["2022M722530"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023T160512"],"award-info":[{"award-number":["2023T160512"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["xtr052023004"],"award-info":[{"award-number":["xtr052023004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["xtr022019002"],"award-info":[{"award-number":["xtr022019002"]}],"id":[{"id":"10.13039\/501100012226","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.2025.3576594","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T17:55:31Z","timestamp":1749059731000},"page":"6005-6018","source":"Crossref","is-referenced-by-count":2,"title":["Robust Adversarial Defenses in Federated Learning: Exploring the Impact of Data Heterogeneity"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-451X","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6979-3537","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0694-3603","authenticated-orcid":false,"given":"Dawei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Macau, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6265-7345","authenticated-orcid":false,"given":"Chenhao","family":"Lin","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-6729","authenticated-orcid":false,"given":"Shuai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0547-315X","authenticated-orcid":false,"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6959-0569","authenticated-orcid":false,"given":"Chao","family":"Shen","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}]}],"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","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","volume":"2","author":"Li"},{"key":"ref3","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Karimireddy"},{"key":"ref4","article-title":"Backdoor federated learning by poisoning backdoor-critical layers","author":"Zhuang","year":"2023","journal-title":"arXiv:2308.04466"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909068"},{"key":"ref6","article-title":"DBA: Distributed backdoor attacks against federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xie"},{"key":"ref7","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2021.100002"},{"key":"ref10","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":"ref11","first-page":"8632","article-title":"A little is enough: Circumventing defenses for distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Baruch"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"ref14","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref15","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref16","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019","journal-title":"arXiv:1911.07963"},{"key":"ref17","first-page":"11372","article-title":"CRFL: Certifiably robust federated learning against backdoor attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17143-7_20"},{"key":"ref19","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019","journal-title":"arXiv:1907.02189"},{"key":"ref20","article-title":"Model poisoning attacks to federated learning via multi-round consistency","author":"Xie","year":"2024","journal-title":"arXiv:2404.15611"},{"key":"ref21","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. NIPS","author":"Blanchard"},{"key":"ref22","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in Byzantium","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"El Mhamdi"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671906"},{"key":"ref26","article-title":"FedredeFense: Defending against model poisoning attacks for federated learning using model update reconstruction error","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Yueqi"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2022.23054"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2025.241796"},{"key":"ref29","first-page":"12613","article-title":"FL-WBC: Enhancing robustness against model poisoning attacks in federated learning from a client perspective","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sun"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref31","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref33","article-title":"Achieving linear speedup with partial worker participation in non-IID federated learning","author":"Yang","year":"2021","journal-title":"arXiv:2101.11203"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/11024045.pdf?arnumber=11024045","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T06:15:01Z","timestamp":1750745701000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11024045\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3576594","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}