{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:44:59Z","timestamp":1774435499381,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["ZYGX2020ZB025"],"award-info":[{"award-number":["ZYGX2020ZB025"]}]},{"name":"Sichuan Science and Technology Program","award":["2021YFG0157"],"award-info":[{"award-number":["2021YFG0157"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann. Telecommun."],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s12243-022-00929-4","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T01:02:51Z","timestamp":1666746171000},"page":"135-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mitigation of a poisoning attack in federated learning by using historical distance detection"],"prefix":"10.1007","volume":"78","author":[{"given":"Zhaosen","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8785-9015","authenticated-orcid":false,"given":"Xuyang","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fagen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingni","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Canran","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"929_CR1","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, USA. Proceedings of Machine Learning Research, vol 54, pp 1273\u20131282"},{"key":"929_CR2","unstructured":"Blanchard P, Mhamdi EME, Guerraoui R, Stainer JJ (2017)\u00a0Machine learning with adversaries: Byzantine tolerant gradient descent. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9,\u00a0Long Beach, CA, USA, pp 119\u2013129\u00a0"},{"key":"929_CR3","unstructured":"Yin D, Chen Y, Ramchandran K, Bartlett P (2018) Byzantine-robust distributed learning: Towards optimal statistical rates"},{"key":"929_CR4","unstructured":"El Mhamdi EM, Guerraoui R, Rouault S (2018) The hidden vulnerability of distributed learning in byzantium"},{"key":"929_CR5","doi-asserted-by":"publisher","unstructured":"Jagielski M, Oprea A, Biggio B, Liu C, Nita-Rotaru C, Li B (2018) Manipulating machine learning: Poisoning attacks and countermeasures for regression learning, pp. 19\u201335 https:\/\/doi.org\/10.1109\/SP.2018.00057","DOI":"10.1109\/SP.2018.00057"},{"key":"929_CR6","unstructured":"Biggio B, Nelson B, Laskov P (2012) Poisoning attacks against support vector machines"},{"key":"929_CR7","unstructured":"Chen X, Liu C, Li B, Lu K, Song D (2017) Targeted backdoor attacks on deep learning systems using data poisoning"},{"key":"929_CR8","doi-asserted-by":"crossref","unstructured":"Nasr M, Shokri R, Houmansadr A (2018) Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning","DOI":"10.1109\/SP.2019.00065"},{"key":"929_CR9","doi-asserted-by":"crossref","unstructured":"Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP)","DOI":"10.1109\/SP.2017.41"},{"key":"929_CR10","doi-asserted-by":"publisher","unstructured":"Melis L, Song C, De Cristofaro E, Shmatikov V (2019) Exploiting unintended feature leakage in collaborative learning, pp 691\u2013706. https:\/\/doi.org\/10.1109\/SP.2019.00029","DOI":"10.1109\/SP.2019.00029"},{"key":"929_CR11","doi-asserted-by":"publisher","unstructured":"Zhu L, Han S (2020) Deep Leakage from Gradients, pp 17\u201331 https:\/\/doi.org\/10.1007\/978-3-030-63076-8_2","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"929_CR12","doi-asserted-by":"publisher","unstructured":"Shi Z, Ding X, Li F, Chen Y, Li C (2021) Mitigation of poisoning attack in federated learning by using historical distance detection. In: 2021 5th Cyber Security in Networking Conference (CSNet), pp 10\u201317. https:\/\/doi.org\/10.1109\/CSNet52717.2021.9614278","DOI":"10.1109\/CSNet52717.2021.9614278"},{"key":"929_CR13","doi-asserted-by":"publisher","unstructured":"Kairouz P, McMahan H (2021) Advances and open problems in federated learning. Foundations and Trends in Machine Learning 14. https:\/\/doi.org\/10.1561\/2200000083","DOI":"10.1561\/2200000083"},{"key":"929_CR14","unstructured":"Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V (2020)\u00a0 How to backdoor federated learning. In: Chiappa S, Calandra R (eds)\u00a0 The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26\u201328 August 2020, Online [Palermo, Sicily, Italy]. Proceedings of Machine Learning Research, vol. 108, pp 2938\u20132948"},{"key":"929_CR15","doi-asserted-by":"publisher","unstructured":"Zhang J, Chen J, Wu D, Chen B, Yu S (2019) Poisoning attack in federated learning using generative adversarial nets, pp 374\u2013380. https:\/\/doi.org\/10.1109\/TrustCom\/BigDataSE.2019.00057","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00057"},{"key":"929_CR16","unstructured":"Xie C, Koyejo O, Gupta I 2019 Fall of empires: Breaking byzantine-tolerant SGD by inner product manipulation. In: Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25. Proceedings of Machine Learning Research, vol 115, pp 261\u2013270"},{"key":"929_CR17","unstructured":"Sun Z, Kairouz P, Suresh AT, McMahan HB (2019) Can you really backdoor federated learning? CoRR abs\/1911.07963"},{"key":"929_CR18","unstructured":"Bhagoji AN, Chakraborty S, Mittal P, Calo SB (2019) Analyzing federated learning through an adversarial lens. In: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9\u201315 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, pp 634\u2013643"},{"key":"929_CR19","unstructured":"Fung C, Yoon CJM, Beschastnikh I (2018) Mitigating sybils in federated learning poisoning. CoRR abs\/1808.04866"},{"key":"929_CR20","doi-asserted-by":"publisher","unstructured":"Shen S, Tople S, Saxena P (2016) A uror: defending against poisoning attacks in collaborative deep learning systems, pp 508\u2013519. https:\/\/doi.org\/10.1145\/2991079.2991125","DOI":"10.1145\/2991079.2991125"},{"key":"929_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TSP.2020.3012952","volume":"68","author":"Z Wu","year":"2020","unstructured":"Wu Z, Ling Q, Chen T, Giannakis G (2020) Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks. IEEE Trans Signal Process 68:1\u20131. https:\/\/doi.org\/10.1109\/TSP.2020.3012952","journal-title":"IEEE Trans Signal Process"},{"key":"929_CR22","doi-asserted-by":"publisher","unstructured":"Cao D, Chang S, Lin Z, Liu G, Sun D (2019) Understanding distributed poisoning attack in federated learning, pp 233\u2013239. https:\/\/doi.org\/10.1109\/ICPADS47876.2019.00042","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"929_CR23","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5906","volume-title":"Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Chen J, Zhang J, Wu D, Blumenstein M, Yu S (2020) Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks. Practice and Experience, Concurrency and Computation. https:\/\/doi.org\/10.1002\/cpe.5906"},{"key":"929_CR24","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-030-86890-1_3","volume-title":"Information and Communications Security","author":"A Manna","year":"2021","unstructured":"Manna A, Kasyap H, Tripathy S (2021) Moat: Model agnostic defense against targeted poisoning attacks in federated learning. In: Gao D, Li Q, Guan X, Liao X (eds) Information and Communications Security. Springer, pp 38\u201355"},{"key":"929_CR25","doi-asserted-by":"publisher","unstructured":"Chen L-Y, Chiu T-C, Pang A-C, Cheng L-C (2021) Fedequal: Defending model poisoning attacks in heterogeneous federated learning. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp 1\u20136.\u00a0https:\/\/doi.org\/10.1109\/GLOBECOM46510.2021.9685082","DOI":"10.1109\/GLOBECOM46510.2021.9685082"},{"key":"929_CR26","doi-asserted-by":"publisher","unstructured":"You X, Liu Z, Yang X, Ding X (2022) Poisoning attack detection using client historical similarity in non-iid environments. In: 2022 12th international conference on cloud computing, data science & engineering (confluence), pp 439\u2013447 https:\/\/doi.org\/10.1109\/Confluence52989.2022.9734158","DOI":"10.1109\/Confluence52989.2022.9734158"},{"key":"929_CR27","doi-asserted-by":"publisher","unstructured":"Liu W, Lin H, Wang X, Hu J, Kaddoum G, Piran MJ, Alamri A (2021) D2mif: A malicious model detection mechanism for federated learning empowered artificial intelligence of things. IEEE Internet of Things Journal 1\u20131. https:\/\/doi.org\/10.1109\/JIOT.2021.3081606","DOI":"10.1109\/JIOT.2021.3081606"},{"key":"929_CR28","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1007\/978-0-387-30164-8_271","volume":"2006","author":"D Chakrabarti","year":"2006","unstructured":"Chakrabarti D, Kumar R (2006) Tomkins A. Evolutionary clustering 2006:554\u2013560. https:\/\/doi.org\/10.1007\/978-0-387-30164-8_271","journal-title":"Evolutionary clustering"},{"issue":"5","key":"929_CR29","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1109\/TDSC.2020.2986205","volume":"18","author":"L Zhao","year":"2021","unstructured":"Zhao L, Hu S, Wang Q, Jiang J, Shen C, Luo X, Hu P (2021) Shielding collaborative learning: Mitigating poisoning attacks through client-side detection. IEEE Trans Dependable Secure Comput 18(5):2029\u20132041. https:\/\/doi.org\/10.1109\/TDSC.2020.2986205","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"929_CR30","doi-asserted-by":"publisher","first-page":"4574","DOI":"10.1109\/TIFS.2021.3108434","volume":"16","author":"X Liu","year":"2021","unstructured":"Liu X, Li H, Xu G, Chen Z, Huang X, Lu R (2021) Privacy-enhanced federated learning against poisoning adversaries. IEEE Trans Inf Forensics Secur 16:4574\u20134588. https:\/\/doi.org\/10.1109\/TIFS.2021.3108434","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"929_CR31","unstructured":"Fang M, Cao X, Jia J, Gong NZ 2020 Local model poisoning attacks to byzantine-robust federated learning. In: 29th USENIX Security Symposium, USENIX Security 2020, pp 1605\u20131622"}],"container-title":["Annals of Telecommunications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12243-022-00929-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12243-022-00929-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12243-022-00929-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T03:17:47Z","timestamp":1679368667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12243-022-00929-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":31,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["929"],"URL":"https:\/\/doi.org\/10.1007\/s12243-022-00929-4","relation":{},"ISSN":["0003-4347","1958-9395"],"issn-type":[{"value":"0003-4347","type":"print"},{"value":"1958-9395","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"7 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}