{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:23:52Z","timestamp":1761402232880,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,18]]},"DOI":"10.1145\/3476883.3520221","type":"proceedings-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T02:11:49Z","timestamp":1651716709000},"page":"238-242","source":"Crossref","is-referenced-by-count":5,"title":["Robust federated learning based on metrics learning and unsupervised clustering for malicious data detection"],"prefix":"10.1145","author":[{"given":"Jiaming","family":"Li","sequence":"first","affiliation":[{"name":"Kennesaw State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Kennesaw State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Kennesaw State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org.  Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org."},{"key":"e_1_3_2_1_2_1","volume-title":"Deep Learning Using Rectified Linear Units (relu). arXiv preprint arXiv:1803.08375","author":"Agarap Abien Fred","year":"2018","unstructured":"Abien Fred Agarap . 2018. Deep Learning Using Rectified Linear Units (relu). arXiv preprint arXiv:1803.08375 ( 2018 ). Abien Fred Agarap. 2018. Deep Learning Using Rectified Linear Units (relu). arXiv preprint arXiv:1803.08375 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"Titouan Parcollet, Pedro Porto Buarque de Gusm\u00e3o, and Nicholas D. Lane.","author":"Beutel Daniel J.","year":"2021","unstructured":"Daniel J. Beutel , Taner Topal , Akhil Mathur , Xinchi Qiu , Javier Fernandez-Marques , Yan Gao , Lorenzo Sani , Kwing Hei Li , Titouan Parcollet, Pedro Porto Buarque de Gusm\u00e3o, and Nicholas D. Lane. 2021 . Flower : A Friendly Federated Learning Research Framework . arXiv:2007.14390 [cs.LG] Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusm\u00e3o, and Nicholas D. Lane. 2021. Flower: A Friendly Federated Learning Research Framework. arXiv:2007.14390 [cs.LG]"},{"key":"e_1_3_2_1_4_1","volume-title":"Ioannis Ch Paschalidis, and Wei Shi","author":"Brisimi Theodora","year":"2018","unstructured":"Theodora Brisimi , Ruidi Chen , Theofanie Mela , Alex Olshevsky , Ioannis Ch Paschalidis, and Wei Shi . 2018 . Federated Learning of Predictive Models from Federated Electronic Health Records. International journal of medical informatics 112 (2018), 59--67. Theodora Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi. 2018. Federated Learning of Predictive Models from Federated Electronic Health Records. International journal of medical informatics 112 (2018), 59--67."},{"key":"e_1_3_2_1_5_1","volume-title":"29th USENIX Security Symposium (USENIX Security 20)","author":"Fang Minghong","year":"2020","unstructured":"Minghong Fang , Xiaoyu Cao , Jinyuan Jia , and Neil Gong . 2020 . Local Model Poisoning Attacks to Byzantine-robust Federated Learning . In 29th USENIX Security Symposium (USENIX Security 20) . Virtual Event, USA, 1605--1622. Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020. Local Model Poisoning Attacks to Byzantine-robust Federated Learning. In 29th USENIX Security Symposium (USENIX Security 20). Virtual Event, USA, 1605--1622."},{"key":"e_1_3_2_1_6_1","volume-title":"Federated Learning for Mobile Keyboard Prediction. arXiv preprint arXiv:1811.03604","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard , Kanishka Rao , Rajiv Mathews , Swaroop Ramaswamy , Fran\u00e7oise Beaufays , Sean Augenstein , Hubert Eichner , Chlo\u00e9 Kiddon , and Daniel Ramage . 2018. Federated Learning for Mobile Keyboard Prediction. arXiv preprint arXiv:1811.03604 ( 2018 ). Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Fran\u00e7oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018. Federated Learning for Mobile Keyboard Prediction. arXiv preprint arXiv:1811.03604 (2018)."},{"key":"e_1_3_2_1_7_1","unstructured":"Elad Hoffer and Nir Ailon. 2018. Deep Metric Learning Using Triplet Network. arXiv:1412.6622 [cs.LG]  Elad Hoffer and Nir Ailon. 2018. Deep Metric Learning Using Triplet Network. arXiv:1412.6622 [cs.LG]"},{"key":"e_1_3_2_1_8_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2017","unstructured":"Diederik P. Kingma and Jimmy Ba . 2017 . Adam : A Method for Stochastic Optimization . arXiv:1412.6980 [cs.LG] Diederik P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs.LG]"},{"key":"e_1_3_2_1_9_1","unstructured":"Alex Krizhevsky Vinod Nair and Geoffrey Hinton. [n.d.]. CIFAR-10 (Canadian Institute for Advanced Research). ([n. d.]). http:\/\/www.cs.toronto.edu\/~kriz\/cifar.html  Alex Krizhevsky Vinod Nair and Geoffrey Hinton. [n.d.]. CIFAR-10 (Canadian Institute for Advanced Research). ([n. d.]). http:\/\/www.cs.toronto.edu\/~kriz\/cifar.html"},{"key":"e_1_3_2_1_10_1","volume-title":"Learning to Detect Malicious Clients for Robust Federated Learning. arXiv preprint arXiv:2002.00211","author":"Li Suyi","year":"2020","unstructured":"Suyi Li , Yong Cheng , Wei Wang , Yang Liu , and Tianjian Chen . 2020. Learning to Detect Malicious Clients for Robust Federated Learning. arXiv preprint arXiv:2002.00211 ( 2020 ). Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. 2020. Learning to Detect Malicious Clients for Robust Federated Learning. arXiv preprint arXiv:2002.00211 (2020)."},{"key":"e_1_3_2_1_11_1","volume-title":"Auto-weighted Robust Federated Learning with Corrupted Data Sources. arXiv preprint arXiv:2101.05880","author":"Li Shenghui","year":"2021","unstructured":"Shenghui Li , Edith Ngai , Fanghua Ye , and Thiemo Voigt . 2021. Auto-weighted Robust Federated Learning with Corrupted Data Sources. arXiv preprint arXiv:2101.05880 ( 2021 ). Shenghui Li, Edith Ngai, Fanghua Ye, and Thiemo Voigt. 2021. Auto-weighted Robust Federated Learning with Corrupted Data Sources. arXiv preprint arXiv:2101.05880 (2021)."},{"key":"e_1_3_2_1_12_1","volume-title":"Byzantine-robust Federated Learning Through Spatial-temporal Analysis of Local Model Updates. arXiv preprint arXiv:2107.01477","author":"Li Zhuohang","year":"2021","unstructured":"Zhuohang Li , Luyang Liu , Jiaxin Zhang , and Jian Liu . 2021. Byzantine-robust Federated Learning Through Spatial-temporal Analysis of Local Model Updates. arXiv preprint arXiv:2107.01477 ( 2021 ). Zhuohang Li, Luyang Liu, Jiaxin Zhang, and Jian Liu. 2021. Byzantine-robust Federated Learning Through Spatial-temporal Analysis of Local Model Updates. arXiv preprint arXiv:2107.01477 (2021)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_3_2_1_14_1","volume-title":"20th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR","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 20th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR , Fort Lauderdale, USA, 1273--1282. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient Learning of Deep Networks from Decentralized Data. In 20th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, Fort Lauderdale, USA, 1273--1282."},{"key":"e_1_3_2_1_15_1","unstructured":"Keiron O'Shea and Ryan Nash. 2015. An Introduction to Convolutional Neural Networks. arXiv:1511.08458 [cs.NE]  Keiron O'Shea and Ryan Nash. 2015. An Introduction to Convolutional Neural Networks. arXiv:1511.08458 [cs.NE]"},{"key":"e_1_3_2_1_16_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:cs.LG\/1708.07747 [cs.LG]  Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:cs.LG\/1708.07747 [cs.LG]"}],"event":{"name":"ACM SE '22: 2022 ACM Southeast Conference","sponsor":["ACM Association for Computing Machinery"],"location":"Virtual Event","acronym":"ACM SE '22"},"container-title":["Proceedings of the ACM Southeast Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3476883.3520221","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3476883.3520221","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:45Z","timestamp":1750188645000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3476883.3520221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":16,"alternative-id":["10.1145\/3476883.3520221","10.1145\/3476883"],"URL":"https:\/\/doi.org\/10.1145\/3476883.3520221","relation":{},"subject":[],"published":{"date-parts":[[2022,4,18]]}}}