{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:18:27Z","timestamp":1778372307745,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Guangxi Science and Technology Major Project","award":["No. AA22068070"],"award-info":[{"award-number":["No. AA22068070"]}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Nos. 62166004,U21A20474"],"award-info":[{"award-number":["Nos. 62166004,U21A20474"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,24]]},"DOI":"10.1145\/3670105.3670211","type":"proceedings-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T18:29:36Z","timestamp":1722277776000},"page":"606-612","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6154-0855","authenticated-orcid":false,"given":"Xiaoyun","family":"Gan","sequence":"first","affiliation":[{"name":"Guangxi Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6014-0290","authenticated-orcid":false,"given":"Shanyu","family":"Gan","sequence":"additional","affiliation":[{"name":"Guangxi Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7646-5664","authenticated-orcid":false,"given":"Taizhi","family":"Su","sequence":"additional","affiliation":[{"name":"Guangxi Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2583-9112","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangxi Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"2948","author":"Bagdasaryan Eugene","year":"2020","unstructured":"Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How To Backdoor Federated Learning. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 108), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 2938\u20132948. https:\/\/proceedings.mlr.press\/v108\/bagdasaryan20a.html"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Blanchard Peva","year":"2017","unstructured":"Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine learning with adversaries: byzantine tolerant gradient descent. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS\u201917). Curran Associates Inc., 118\u2013128."},{"key":"e_1_3_2_1_3_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representa- tions. arXiv:2002.05709 [cs.LG]"},{"key":"e_1_3_2_1_4_1","volume-title":"The Limitations of Federated Learning in Sybil Settings. In International Symposium on Recent Advances in Intrusion Detection. https:\/\/api.semanticscholar.org\/CorpusID:221542915","author":"Fung Clement","year":"2020","unstructured":"Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh. 2020. The Limitations of Federated Learning in Sybil Settings. In International Symposium on Recent Advances in Intrusion Detection. https:\/\/api.semanticscholar.org\/CorpusID:221542915"},{"key":"e_1_3_2_1_5_1","volume-title":"Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821","author":"Gao T","year":"2021","unstructured":"T Gao, X Yao, and D Chen. 2021. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021). https: \/\/arxiv.org\/abs\/2104.08821"},{"key":"e_1_3_2_1_6_1","volume-title":"Generative adversarial networks. 63, 11 (oct","author":"Goodfellow Ian","year":"2020","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. 63, 11 (oct 2020), 139\u2013144."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"e_1_3_2_1_8_1","unstructured":"Suyi Li Yong Cheng Yang Liu Wei Wang and Tianjian Chen. 2019. Abnormal Client Behavior Detection in Federated Learning. arXiv:1910.09933 [cs.LG]"},{"key":"e_1_3_2_1_9_1","unstructured":"Yiming Li Tongqing Zhai Baoyuan Wu Yong Jiang Zhifeng Li and Shutao Xia. 2021. Rethinking the Trigger of Backdoor Attack. arXiv:2004.04692 [cs.CR]"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 54). PMLR, 1273\u20131282. https:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"e_1_3_2_1_12_1","volume-title":"Emiliano De Cristofaro, and Vitaly Shmatikov","author":"Melis Luca","year":"2018","unstructured":"Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2018. Exploiting Unintended Feature Leakage in Collaborative Learning. arXiv:1805.04049 [cs.CR]"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107166"},{"key":"e_1_3_2_1_14_1","volume-title":"Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020 [cs.CV]"},{"key":"e_1_3_2_1_15_1","volume-title":"Ananda Theertha Suresh, and H. Brendan McMahan","author":"Sun Ziteng","year":"2019","unstructured":"Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H. Brendan McMahan. 2019. Can You Really Backdoor Federated Learning? arXiv:1911.07963 [cs.LG]"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkgyS0VFvr","author":"Xie Chulin","year":"2020","unstructured":"Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li. 2020. DBA: Distributed Backdoor Attacks against Federated Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkgyS0VFvr"},{"key":"e_1_3_2_1_18_1","unstructured":"Mang Ye Xiuwen Fang Bo Du Pong C. Yuen and Dacheng Tao. 2023. Heterogeneous Federated Learning: State-of-the-art and Research Challenges. arXiv:2307.10616 [cs.LG]"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413546"}],"event":{"name":"CNIOT 2024: 2024 5th International Conference on Computing, Networks and Internet of Things","location":"Tokyo Japan","acronym":"CNIOT 2024"},"container-title":["Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3670105.3670211","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3670105.3670211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:51:17Z","timestamp":1755877877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3670105.3670211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":19,"alternative-id":["10.1145\/3670105.3670211","10.1145\/3670105"],"URL":"https:\/\/doi.org\/10.1145\/3670105.3670211","relation":{},"subject":[],"published":{"date-parts":[[2024,5,24]]},"assertion":[{"value":"2024-07-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}