{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T14:21:48Z","timestamp":1730211708023,"version":"3.28.0"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,12]]},"DOI":"10.1109\/csnet52717.2021.9614278","type":"proceedings-article","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T21:45:17Z","timestamp":1637703917000},"page":"10-17","source":"Crossref","is-referenced-by-count":2,"title":["Mitigation of poisoning attack in federated learning by using historical distance detection"],"prefix":"10.1109","author":[{"given":"Zhaosen","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"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":"263","reference":[{"key":"ref10","first-page":"12","author":"lyu","year":"0","journal-title":"Privacy and robustness in federated learning Attacks and defenses"},{"journal-title":"How to backdoor federated learning","year":"0","author":"bagdasaryan","key":"ref11"},{"key":"ref12","first-page":"374","author":"zhang","year":"0","journal-title":"Poisoning attack in federated learning using generative adversarial nets"},{"journal-title":"Fall of empires Breaking byzantine-tolerant sgd by inner product manipulation","year":"0","author":"xie","key":"ref13"},{"journal-title":"Can you really backdoor federated learning?","year":"0","author":"sun","key":"ref14"},{"journal-title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","year":"0","author":"fang","key":"ref15"},{"journal-title":"Analyzing federated learning through an adversarial lens","year":"0","author":"bhagoji","key":"ref16"},{"journal-title":"Mitigating sybils in federated learning poisoning","year":"0","author":"fung","key":"ref17"},{"key":"ref18","first-page":"508","author":"shen","year":"0","journal-title":"A uror defending against poisoning attacks in collaborative deep learning systems"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref4","first-page":"3518","article-title":"The hidden vulnerability of distributed learning in byzantium","volume":"80","author":"mhamdi","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning ICML 2018"},{"journal-title":"Byzantine-robust distributed learning Towards optimal statistical rates","year":"0","author":"yin","key":"ref3"},{"journal-title":"Poisoning Attacks Against Support Vector Machines","year":"0","author":"biggio","key":"ref6"},{"key":"ref5","first-page":"19","author":"jagielski","year":"0","journal-title":"Manipulating machine learning Poisoning attacks and countermeasures for regression learning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"journal-title":"Targeted backdoor attacks on deep learning systems using data poisoning","year":"0","author":"chen","key":"ref7"},{"key":"ref2","first-page":"119","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","author":"blanchard","year":"0","journal-title":"Advances in Neural Information Processing Systems 30 Annual Conference on Neural Information Processing Systems 2017 December 4-9 2017 Long Beach CA USA"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"mcmahan","year":"2017","journal-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics AISTATS 2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref20","first-page":"233","author":"cao","year":"0","journal-title":"Understanding distributed poisoning attack in federated learning"},{"key":"ref22","first-page":"554","volume":"2006","author":"chakrabarti","year":"2006","journal-title":"Evolutionary clustering"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5906"}],"event":{"name":"2021 5th Cyber Security in Networking Conference (CSNet)","start":{"date-parts":[[2021,10,12]]},"location":"Abu Dhabi, United Arab Emirates","end":{"date-parts":[[2021,10,14]]}},"container-title":["2021 5th Cyber Security in Networking Conference (CSNet)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9614640\/9614271\/09614278.pdf?arnumber=9614278","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:52:38Z","timestamp":1652201558000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9614278\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,12]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/csnet52717.2021.9614278","relation":{},"subject":[],"published":{"date-parts":[[2021,10,12]]}}}