{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:28:19Z","timestamp":1780054099668,"version":"3.54.0"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"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":["62272203"],"award-info":[{"award-number":["62272203"]}],"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":["62372105"],"award-info":[{"award-number":["62372105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Leading-edge Technology Program of Jiangsu Natural Science Foundation","award":["BK20202001"],"award-info":[{"award-number":["BK20202001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Serv. Comput."],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1109\/tsc.2024.3376255","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:47:54Z","timestamp":1710269274000},"page":"1480-1491","source":"Crossref","is-referenced-by-count":24,"title":["DPFLA: Defending Private Federated Learning Against Poisoning Attacks"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3677-6823","authenticated-orcid":false,"given":"Xia","family":"Feng","sequence":"first","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1721-4120","authenticated-orcid":false,"given":"Wenhao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Macau, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9439-8256","authenticated-orcid":false,"given":"Chunjie","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Hainan University, Haikou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0048-5979","authenticated-orcid":false,"given":"Liangmin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4960-174X","authenticated-orcid":false,"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-020-00082-4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_17"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3081560"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/s20216230"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3196274"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3315125"},{"key":"ref11","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2022.23054"},{"key":"ref13","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Blanchard"},{"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","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"ref16","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":"ref17","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110178"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417885"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP57164.2023.00020"},{"key":"ref23","article-title":"Poisoning attacks against support vector machines","author":"Biggio","year":"2012"},{"key":"ref24","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539402"},{"key":"ref26","article-title":"sklearn.decomposition.PCA \u2013 scikit-learn 1.3.2 documentation","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2307\/2005398"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00155"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref32","first-page":"1","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref33","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"}],"container-title":["IEEE Transactions on Services Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4629386\/10630587\/10470437.pdf?arnumber=10470437","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T06:33:10Z","timestamp":1723185190000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10470437\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":33,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tsc.2024.3376255","relation":{},"ISSN":["1939-1374","2372-0204"],"issn-type":[{"value":"1939-1374","type":"electronic"},{"value":"2372-0204","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}