{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:06:27Z","timestamp":1757621187856,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819502172"},{"type":"electronic","value":"9789819502189"}],"license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-0218-9_17","type":"book-chapter","created":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T21:09:31Z","timestamp":1754168971000},"page":"223-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Robust Distributed Minimax Learning Method Against Model Poisoning Attacks"],"prefix":"10.1007","author":[{"given":"Tingting","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yifei","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Zhipeng","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Dongxiao","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"17_CR1","unstructured":"Bhagoji, A.N., Chakraborty, S., Mittal, P., Calo, S.: Analyzing federated learning through an adversarial lens. In: International Conference on Machine Learning, pp. 634\u2013643 (2019)"},{"key":"17_CR2","unstructured":"Dai, B., et al.: SBEED: convergent reinforcement learning with nonlinear function approximation. In: International Conference on Machine Learning, pp. 1125\u20131134 (2018)"},{"key":"17_CR3","unstructured":"Deng, Y., Mahdavi, M.: Local stochastic gradient descent ascent: convergence analysis and communication efficiency. In: International Conference on Artificial Intelligence and Statistics, pp. 1387\u20131395 (2021)"},{"key":"17_CR4","unstructured":"Du, S.S., Hu, W.: Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 196\u2013205 (2019)"},{"key":"17_CR5","unstructured":"Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to $$\\{$$Byzantine-Robust$$\\}$$ federated learning. In: 29th USENIX Security Symposium (USENIX Security 2020), pp. 1605\u20131622 (2020)"},{"key":"17_CR6","unstructured":"Fung, C., Yoon, C.J., Beschastnikh, I.: The limitations of federated learning in sybil settings. In: 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020), pp. 301\u2013316 (2020)"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Garc\u00eda\u00a0Trillos, N., Akash, A.K., Li, S., Riedl, K., Zhu, Y.: Defending against diverse attacks in federated learning through consensus-based bi-level optimization. arXiv e-prints pp. arXiv\u20132412 (2024)","DOI":"10.1098\/rsta.2024.0235"},{"key":"17_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol.\u00a027 (2014)"},{"key":"17_CR9","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, vol.\u00a030 (2017)"},{"key":"17_CR10","unstructured":"Le\u00a0Cun, Y., Denker, J., Solla, S.: Optimal brain damage, advances in neural information processing systems. Denver 1989, Ed. D. Touretzsky, Morgan Kaufmann 598, 605 (1990)"},{"key":"17_CR11","unstructured":"Lin, T., Jin, C., Jordan, M.: On gradient descent ascent for nonconvex-concave minimax problems. In: International Conference on Machine Learning, pp. 6083\u20136093 (2020)"},{"key":"17_CR12","unstructured":"Liu, M.L., Mroueh, Y., Zhang, W., Cui, X., Ross, J., Das, P.: Decentralized parallel algorithm for training generative adversarial nets. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 11056\u201311070 (2020)"},{"key":"17_CR13","unstructured":"Mohri, M., Sivek, G., Suresh, A.T.: Agnostic federated learning. In: International Conference on Machine Learning, pp. 4615\u20134625 (2019)"},{"key":"17_CR14","unstructured":"Namkoong, H., Duchi, J.C.: Stochastic gradient methods for distributionally robust optimization with f-divergences. In: Advances in Neural Information Processing Systems, vol.\u00a029 (2016)"},{"key":"17_CR15","unstructured":"Nouiehed, M., Sanjabi, M., Huang, T., Lee, J.D., Razaviyayn, M.: Solving a class of non-convex min-max games using iterative first order methods. In: Advances in Neural Information Processing Systems, vol.\u00a032, pp. 14934\u201314942 (2019)"},{"key":"17_CR16","unstructured":"Panda, A., Mahloujifar, S., Bhagoji, A.N., Chakraborty, S., Mittal, P.: Sparsefed: mitigating model poisoning attacks in federated learning with sparsification. In: International Conference on Artificial Intelligence and Statistics, pp. 7587\u20137624 (2022)"},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1109\/TSP.2022.3153135","volume":"70","author":"K Pillutla","year":"2022","unstructured":"Pillutla, K., Kakade, S.M., Harchaoui, Z.: Robust aggregation for federated learning. IEEE Trans. Signal Process. 70, 1142\u20131154 (2022)","journal-title":"IEEE Trans. Signal Process."},{"key":"17_CR18","unstructured":"Rasouli, M., Sun, T., Rajagopal, R.: Fedgan: federated generative adversarial networks for distributed data. arXiv preprint arXiv:2006.07228 (2020)"},{"key":"17_CR19","unstructured":"Reisizadeh, A., Farnia, F., Pedarsani, R., Jadbabaie, A.: Robust federated learning: the case of affine distribution shifts. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 21554\u201321565 (2020)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Shen, S., Tople, S., Saxena, P.: Auror: defending against poisoning attacks in collaborative deep learning systems. In: Proceedings of the 32nd Annual Conference on Computer Security Applications, pp. 508\u2013519 (2016)","DOI":"10.1145\/2991079.2991125"},{"key":"17_CR21","first-page":"12613","volume":"34","author":"J Sun","year":"2021","unstructured":"Sun, J., Li, A., DiValentin, L., Hassanzadeh, A., Chen, Y., Li, H.: FL-WBC: enhancing robustness against model poisoning attacks in federated learning from a client perspective. Adv. Neural. Inf. Process. Syst. 34, 12613\u201312624 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Tsaknakis, I., Hong, M., Liu, S.: Decentralized min-max optimization: formulations, algorithms and applications in network poisoning attack. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5755\u20135759 (2020)","DOI":"10.1109\/ICASSP40776.2020.9054056"},{"key":"17_CR23","unstructured":"Wu, C., Yang, X., Zhu, S., Mitra, P.: Mitigating backdoor attacks in federated learning. arXiv preprint arXiv:2011.01767 (2020)"},{"key":"17_CR24","unstructured":"Zhu, C., Roos, S., Chen, L.Y.: Leadfl: client self-defense against model poisoning in federated learning. In: International Conference on Machine Learning, pp. 43158\u201343180 (2023)"}],"container-title":["Lecture Notes in Computer Science","Computing and Combinatorics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0218-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T12:40:56Z","timestamp":1757335256000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0218-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"ISBN":["9789819502172","9789819502189"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0218-9_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"3 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COCOON","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Computing and Combinatorics Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cocoon0","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/tcsuestc.com\/cocoon2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}