{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T13:30:39Z","timestamp":1777210239160,"version":"3.51.4"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206238"],"award-info":[{"award-number":["62206238"]}],"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":["62076125"],"award-info":[{"award-number":["62076125"]}],"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":["62032025"],"award-info":[{"award-number":["62032025"]}],"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\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220562"],"award-info":[{"award-number":["BK20220562"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Research Project of Universities in Jiangsu Province","award":["22KJB520010"],"award-info":[{"award-number":["22KJB520010"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M732985"],"award-info":[{"award-number":["2023M732985"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Massive Personalized Customization System and Technology","award":["H&C-MPC- 2023-02-05"],"award-info":[{"award-number":["H&C-MPC- 2023-02-05"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20210324134810028"],"award-info":[{"award-number":["JCYJ20210324134810028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tifs.2024.3384846","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T18:31:22Z","timestamp":1712341882000},"page":"4752-4766","source":"Crossref","is-referenced-by-count":31,"title":["<i>FLPurifier<\/i>: Backdoor Defense in Federated Learning via Decoupled Contrastive Training"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2143-5666","authenticated-orcid":false,"given":"Jiale","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Yangzhou University, Yangzhou, China"}]},{"given":"Chengcheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, Yangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5165-5080","authenticated-orcid":false,"given":"Xiaobing","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, Yangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9274-7325","authenticated-orcid":false,"given":"Chunpeng","family":"Ge","sequence":"additional","affiliation":[{"name":"Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Software School, Shandong University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2863-5441","authenticated-orcid":false,"given":"Bing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1562-5105","authenticated-orcid":false,"given":"Willy","family":"Susilo","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, Institute of Cybersecurity and Cryptology, University of Wollongong, Wollongong, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4485-6743","authenticated-orcid":false,"given":"Shui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computing Science, University of Technology Sydney, Ultimo, NSW, Australia"}]}],"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.","author":"McMahan"},{"issue":"1","key":"ref2","first-page":"10320","article-title":"Fate: An industrial grade platform for collaborative learning with data protection","volume":"22","author":"Liu","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref3","article-title":"BadNets: Identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017","journal-title":"arXiv:1708.06733"},{"key":"ref4","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statistics","author":"Bagdasaryan"},{"key":"ref5","first-page":"1","article-title":"DBA: Distributed backdoor attacks against federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xie"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"ref7","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. USENIX Security","author":"Fang"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26083"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179401"},{"key":"ref10","first-page":"301","article-title":"The limitations of federated learning in Sybil settings","volume-title":"Proc. RAID","author":"Fung"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS51616.2021.00086"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2022.23156"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17118"},{"key":"ref15","article-title":"Mitigating backdoor attacks in federated learning","author":"Wu","year":"2020","journal-title":"arXiv:2011.01767"},{"key":"ref16","article-title":"Federated unlearning with knowledge distillation","author":"Wu","year":"2022","journal-title":"arXiv:2201.09441"},{"key":"ref17","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref18","first-page":"16070","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3023126"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01478"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/242"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/554"},{"key":"ref23","first-page":"26429","article-title":"Neurotoxin: Durable backdoors in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/306"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS54753.2022.9798217"},{"key":"ref26","first-page":"1","article-title":"Spectral signatures in backdoor attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Tran"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3021407"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102819"},{"key":"ref29","first-page":"11372","article-title":"CRFL: Certifiably robust federated learning against backdoor attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103366"},{"key":"ref31","first-page":"1","article-title":"Post-training detection of backdoor attacks for two-class and multi-attack scenarios","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xiang"},{"key":"ref32","first-page":"1","article-title":"AEVA: Black-box backdoor detection using adversarial extreme value analysis","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Guo"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01458"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01616"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"ref36","first-page":"1","article-title":"Neural attention distillation: Erasing backdoor triggers from deep neural networks","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796974"},{"key":"ref38","first-page":"1","article-title":"Backdoor defense via decoupling the training process","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Huang"},{"key":"ref39","first-page":"14900","article-title":"Anti-backdoor learning: Training clean models on poisoned data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Li"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179451"},{"key":"ref41","first-page":"1415","article-title":"FLAME: Taming backdoors in federated learning","volume-title":"Proc. 31st USENIX Security Symp.","author":"Nguyen"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref43","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Caron"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref48","first-page":"1","article-title":"Divergence-aware federated self-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhuang"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00821"},{"key":"ref52","first-page":"19332","article-title":"Federated learning from pre-trained models: A contrastive learning approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Tan"},{"key":"ref53","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref54","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref55","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/10319981\/10491118.pdf?arnumber=10491118","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T04:47:34Z","timestamp":1715143654000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10491118\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/tifs.2024.3384846","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}