{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:32:17Z","timestamp":1730327537825,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,25]]},"DOI":"10.1145\/3622896.3622901","type":"proceedings-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T04:12:46Z","timestamp":1696392766000},"page":"28-34","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ANA: An Adaptive Non-outlier-detection-based Aggregation Algorithm Against Poisoning Attack for Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0009-0001-5472-6056","authenticated-orcid":false,"given":"Chengnuo","family":"Deng","sequence":"first","affiliation":[{"name":"Beihang University, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0009-5272-6202","authenticated-orcid":false,"given":"Xiaofeng","family":"Pan","sequence":"additional","affiliation":[{"name":"China Mobile Information Technology Co.Ltd, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0009-2916-6999","authenticated-orcid":false,"given":"Keke","family":"Ye","sequence":"additional","affiliation":[{"name":"China Mobile Information Technology Co.Ltd, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4585-2533","authenticated-orcid":false,"given":"Hongmin","family":"Gao","sequence":"additional","affiliation":[{"name":"China Mobile Information Technology Co.Ltd, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1745-9814","authenticated-orcid":false,"given":"Haiquan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Poushter J. 2016. Smartphone ownership and internet usage continues to climb in emerging economies.Pew research center 22(1) 1-44. https:\/\/www.diapoimansi.gr\/PDF\/pew_research%201.pdf Poushter J. 2016. Smartphone ownership and internet usage continues to climb in emerging economies.Pew research center 22(1) 1-44. https:\/\/www.diapoimansi.gr\/PDF\/pew_research%201.pdf"},{"key":"e_1_3_2_1_2_1","unstructured":"Blanchard P. El Mhamdi E. M. Guerraoui R. & Stainer J. 2017. Machine learning with adversaries: Byzantine tolerant gradient descent. Advances in neural information processing systems 30. Blanchard P. El Mhamdi E. M. Guerraoui R. & Stainer J. 2017. Machine learning with adversaries: Byzantine tolerant gradient descent. Advances in neural information processing systems 30."},{"key":"e_1_3_2_1_3_1","unstructured":"Li B. Wang Y. Singh A. & Vorobeychik Y. 2016. Data poisoning attacks on factorization-based collaborative filtering. Advances in neural information processing systems 29. Li B. Wang Y. Singh A. & Vorobeychik Y. 2016. Data poisoning attacks on factorization-based collaborative filtering. Advances in neural information processing systems 29."},{"key":"#cr-split#-e_1_3_2_1_4_1.1","unstructured":"Chen X. Liu C. Li B. Lu K. & Song D. 2017. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526. https:\/\/doi.org\/10.48550\/arXiv.1712.05526 10.48550\/arXiv.1712.05526"},{"key":"#cr-split#-e_1_3_2_1_4_1.2","unstructured":"Chen X. Liu C. Li B. Lu K. & Song D. 2017. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526. https:\/\/doi.org\/10.48550\/arXiv.1712.05526"},{"key":"e_1_3_2_1_5_1","unstructured":"Baruch G. Baruch M. & Goldberg Y. 2019. A little is enough: Circumventing defenses for distributed learning. Advances in Neural Information Processing Systems 32. Baruch G. Baruch M. & Goldberg Y. 2019. A little is enough: Circumventing defenses for distributed learning. Advances in Neural Information Processing Systems 32."},{"key":"e_1_3_2_1_6_1","unstructured":"McMahan B. Moore E. Ramage D. Hampson S. & y Arcas B. A. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR. McMahan B. Moore E. Ramage D. Hampson S. & y Arcas B. A. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR."},{"key":"e_1_3_2_1_7_1","unstructured":"Kone\u010dn\u00fd J. McMahan H. B. Yu F. X. Richt\u00e1rik P. Suresh A. T. & Bacon D. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492. Kone\u010dn\u00fd J. McMahan H. B. Yu F. X. Richt\u00e1rik P. Suresh A. T. & Bacon D. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492."},{"key":"e_1_3_2_1_8_1","unstructured":"Dean J. Corrado G. Monga R. Chen K. Devin M. Mao M & Ng A. 2012. Large scale distributed deep networks. Advances in neural information processing systems 25. Dean J. Corrado G. Monga R. Chen K. Devin M. Mao M & Ng A. 2012. Large scale distributed deep networks. Advances in neural information processing systems 25."},{"key":"e_1_3_2_1_9_1","first-page":"634","volume-title":"International Conference on Machine Learning(","author":"Bhagoji A. N.","year":"2019","unstructured":"Bhagoji , A. N. , Chakraborty , S. , Mittal , P. , & Calo , S. 2019 . Analyzing federated learning through an adversarial lens . In International Conference on Machine Learning( pp. 634 - 643 ). PMLR. Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. 2019. Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning( pp. 634-643). PMLR."},{"key":"e_1_3_2_1_10_1","first-page":"2938","volume-title":"International Conference on Artificial Intelligence and Statistics(","author":"Bagdasaryan E.","year":"2020","unstructured":"Bagdasaryan , E. , Veit , A. , Hua , Y. , Estrin , D. , & Shmatikov , V. 2020 . How to backdoor federated learning . In International Conference on Artificial Intelligence and Statistics( pp. 2938 - 2948 ). PMLR. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. 2020. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics( pp. 2938-2948). PMLR."},{"key":"e_1_3_2_1_11_1","unstructured":"Xie C. Koyejo O. & Gupta I. 2018. Generalized byzantine-tolerant sgd. arXiv preprint arXiv:1802.10116. Xie C. Koyejo O. & Gupta I. 2018. Generalized byzantine-tolerant sgd. arXiv preprint arXiv:1802.10116."},{"key":"e_1_3_2_1_12_1","first-page":"5650","volume-title":"International Conference on Machine Learning(","author":"Yin D.","year":"2018","unstructured":"Yin , D. , Chen , Y. , Kannan , R. , & Bartlett , P. 2018 . Byzantine-robust distributed learning: Towards optimal statistical rates . In International Conference on Machine Learning( pp. 5650 - 5659 ). PMLR. Yin, D., Chen, Y., Kannan, R., & Bartlett, P. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning( pp. 5650-5659). PMLR."},{"key":"e_1_3_2_1_13_1","first-page":"3521","volume-title":"International Conference on Machine Learning(","author":"Guerraoui R.","year":"2018","unstructured":"Guerraoui , R. , & Rouault , S. 2018 . The hidden vulnerability of distributed learning in byzantium . In International Conference on Machine Learning( pp. 3521 - 3530 ). PMLR. Guerraoui, R., & Rouault, S. 2018. The hidden vulnerability of distributed learning in byzantium. In International Conference on Machine Learning( pp. 3521-3530). PMLR."},{"key":"e_1_3_2_1_14_1","unstructured":"Alistarh D. Allen-Zhu Z. & Li J. 2018. Byzantine stochastic gradient descent. Advances in Neural Information Processing Systems 31. Alistarh D. Allen-Zhu Z. & Li J. 2018. Byzantine stochastic gradient descent. Advances in Neural Information Processing Systems 31."},{"key":"e_1_3_2_1_15_1","volume-title":"Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv preprint arXiv:1912.11279.","author":"Chang H.","year":"2019","unstructured":"Chang , H. , Shejwalkar , V. , Shokri , R. , & Houmansadr , A. 2019 . Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv preprint arXiv:1912.11279. Chang, H., Shejwalkar, V., Shokri, R., & Houmansadr, A. 2019. Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv preprint arXiv:1912.11279."},{"key":"e_1_3_2_1_16_1","unstructured":"He L. Karimireddy S. P. & Jaggi M. 2020. Byzantine-robust learning on heterogeneous datasets via resampling. He L. Karimireddy S. P. & Jaggi M. 2020. Byzantine-robust learning on heterogeneous datasets via resampling."},{"key":"e_1_3_2_1_17_1","first-page":"3521","volume-title":"International Conference on Machine Learning(","author":"Guerraoui R.","year":"2018","unstructured":"Guerraoui , R. , & Rouault , S. 2018 . The hidden vulnerability of distributed learning in byzantium . In International Conference on Machine Learning( pp. 3521 - 3530 ). PMLR. Guerraoui, R., & Rouault, S. 2018. The hidden vulnerability of distributed learning in byzantium. In International Conference on Machine Learning( pp. 3521-3530). PMLR."},{"issue":"2","key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3154503","article-title":"Distributed statistical machine learning in adversarial settings: Byzantine gradient descent","volume":"1","author":"Chen Y.","year":"2017","unstructured":"Chen , Y. , Su , L. , & Xu , J. 2017 . Distributed statistical machine learning in adversarial settings: Byzantine gradient descent . Proceedings of the ACM on Measurement and Analysis of Computing Systems , 1 ( 2 ), 1 - 25 . Chen, Y., Su, L., & Xu, J. 2017. Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 1(2), 1-25.","journal-title":"Proceedings of the ACM on Measurement and Analysis of Computing Systems"},{"key":"e_1_3_2_1_19_1","unstructured":"Mu\u00f1oz-Gonz\u00e1lez L. Co K. T. & Lupu E. C. 2019. Byzantine-robust federated machine learning through adaptive model averaging. arXiv preprint arXiv:1909.05125. Mu\u00f1oz-Gonz\u00e1lez L. Co K. T. & Lupu E. C. 2019. Byzantine-robust federated machine learning through adaptive model averaging. arXiv preprint arXiv:1909.05125."},{"key":"e_1_3_2_1_20_1","first-page":"6893","volume-title":"International Conference on Machine Learning(","author":"Xie C.","year":"2019","unstructured":"Xie , C. , Koyejo , S. , & Gupta , I. 2019 . Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance . In International Conference on Machine Learning( pp. 6893 - 6901 ). PMLR. Xie, C., Koyejo, S., & Gupta, I. 2019. Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance. In International Conference on Machine Learning( pp. 6893-6901). PMLR."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20903"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3489212.3489304"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274706"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140451"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00057"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000263"},{"key":"e_1_3_2_1_27_1","first-page":"941","volume-title":"Blockchain-based decentralized federated learning: A secure and privacy-preserving system. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications","author":"Zhao S.","unstructured":"Zhao , S. , Wu , Y. , Sun , R. , Qian , X. , Zi , D. , Xie , Z. , ... & Han , Z. 2021. Blockchain-based decentralized federated learning: A secure and privacy-preserving system. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications ; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys) ( pp. 941 - 948 ). IEEE. Zhao, S., Wu, Y., Sun, R., Qian, X., Zi, D., Xie, Z., ... & Han, Z. 2021. Blockchain-based decentralized federated learning: A secure and privacy-preserving system. In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys) ( pp. 941-948). IEEE."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Kairouz P. McMahan H. B. Avent B. Bellet A. Bennis M. Bhagoji A. N. ... & Zhao S. 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14(1\u20132) 1-210. Kairouz P. McMahan H. B. Avent B. Bellet A. Bennis M. Bhagoji A. N. ... & Zhao S. 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14(1\u20132) 1-210.","DOI":"10.1561\/2200000083"}],"event":{"name":"CCRIS 2023: 2023 4th International Conference on Control, Robotics and Intelligent System","acronym":"CCRIS 2023","location":"Guangzhou China"},"container-title":["2023 4th International Conference on Control, Robotics and Intelligent System"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3622896.3622901","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T04:18:05Z","timestamp":1696393085000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3622896.3622901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,25]]},"references-count":29,"alternative-id":["10.1145\/3622896.3622901","10.1145\/3622896"],"URL":"https:\/\/doi.org\/10.1145\/3622896.3622901","relation":{},"subject":[],"published":{"date-parts":[[2023,8,25]]},"assertion":[{"value":"2023-10-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}