{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:20:08Z","timestamp":1770351608330,"version":"3.49.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>Distributed intelligence enables the widespread deployment of AI technology, greatly promoting the development of AI. Federated learning is a widely used distributed intelligence technology that allows iterative optimization of global model while protecting user data privacy. Currently, federated learning faces some security threats, as its open architecture provides attackers with opportunities to disrupt the learning process by submitting malicious updates or inserting backdoors. In this article, we propose a robust federated learning method to defend against potential malicious attacks. Specifically, we enhance the algorithm\u2019s performance and stability by implementing localized stepwise updates on the client side and element-wise anomaly detection on the server side. We conducted experiments in a more realistic non-i.i.d. scenario and compared the results with other typical federated learning methods. Our results demonstrate that our approach exhibits strong robustness under non-i.i.d. data distributions, outperforming other methods in terms of test accuracy and resilience to attacks.<\/jats:p>","DOI":"10.1145\/3690822","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T10:25:13Z","timestamp":1725013513000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1950-9478","authenticated-orcid":false,"given":"Shuming","family":"Fan","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0743-7806","authenticated-orcid":false,"given":"Hongjian","family":"Shi","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9523-325X","authenticated-orcid":false,"given":"Chenpei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-8490","authenticated-orcid":false,"given":"Ruhui","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8564-1251","authenticated-orcid":false,"given":"Xiaoming","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Tracking and Telecommunications Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054634"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1912.00818"},{"key":"e_1_3_1_4_2","series-title":"Proceedings of Machine Learning Research","first-page":"2938","volume-title":"23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (26\u201328 August 2020) Online [Palermo, Sicily, Italy]","volume":"108","author":"Bagdasaryan Eugene","year":"2020","unstructured":"Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (26\u201328 August 2020) Online [Palermo, Sicily, Italy](Proceedings of Machine Learning Research, Vol. 108), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, Virtual Event, 2938\u20132948. http:\/\/proceedings.mlr.press\/v108\/bagdasaryan20a.html"},{"key":"e_1_3_1_5_2","first-page":"119","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017(December 4\u20139, 2017, Long Beach, CA)","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 Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017(December 4\u20139, 2017, Long Beach, CA), Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). Neural Information Processing Systems Foundation, Long Beach, CA, 119\u2013129."},{"key":"e_1_3_1_6_2","first-page":"374","volume-title":"Machine Learning and Systems 2019 (MLSys 2019) (Stanford, CA, , March 31\u2013April 2, 2019","author":"Bonawitz Kallista A.","year":"2019","unstructured":"Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chlo\u00e9 Kiddon, Jakub Kone\u010dn\u00fd, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander. 2019. Towards federated learning at scale: System design. In Machine Learning and Systems 2019 (MLSys 2019) (Stanford, CA, , March 31\u2013April 2, 2019) Ameet Talwalkar, Virginia Smith, and Matei Zaharia (Eds.). mlsys.org, Stanford, CA, 374\u2013388. https:\/\/proceedings.mlsys.org\/book\/271.pdf"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20936-9_5"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1812.01097"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/647"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/PST52912.2021.9647826"},{"issue":"1","key":"e_1_3_1_14_2","article-title":"NIST special database 19-hand-printed forms and characters database","author":"Grother Patrick J.","year":"1995","unstructured":"Patrick J. Grother. 1995. NIST special database 19-hand-printed forms and characters database. Technical Report, National Institute of Standards and Technology1 (1995).","journal-title":"Technical Report, National Institute of Standards and Technology"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3472883.3486990"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"e_1_3_1_17_2","series-title":"Proceedings of Machine Learning Research","first-page":"5132","volume-title":"37th International Conference on Machine Learning (ICML 2020) (13\u201318 July 2020), Virtual Event","volume":"119","author":"Karimireddy Sai Praneeth","year":"2020","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh. 2020. SCAFFOLD: Stochastic controlled averaging for federated learning. In 37th International Conference on Machine Learning (ICML 2020) (13\u201318 July 2020), Virtual Event(Proceedings of Machine Learning Research, Vol. 119). PMLR, Virtual Event, 5132\u20135143. http:\/\/proceedings.mlr.press\/v119\/karimireddy20a.html"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/a15080273"},{"key":"e_1_3_1_19_2","first-page":"19","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. 2021. OORT: Efficient federated learning via guided participant selection. In 15th USENIX Symposium on Operating Systems Design and Implementation. USENIX Association, Pisa, Italy, 19\u201335."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13074422"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1910.03581"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"e_1_3_1_24_2","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems 2 (2020), 429\u2013450.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2023.05.006"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3175945"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22879"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988525"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3198176"},{"key":"e_1_3_1_30_2","first-page":"1273","volume-title":"Artificial Intelligence and Statistics","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 Artificial Intelligence and Statistics. PMLR, Fort Lauderdale, FL, 1273\u20131282."},{"key":"e_1_3_1_31_2","first-page":"1415","volume-title":"31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10\u201312, 2022","author":"Nguyen Thien Duc","year":"2022","unstructured":"Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen M\u00f6llering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Shaza Zeitouni, Farinaz Koushanfar, Ahmad-Reza Sadeghi, and Thomas Schneider. 2022. FLAME: Taming backdoors in federated learning. In 31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10\u201312, 2022, Kevin R. B. Butler and Kurt Thomas (Eds.). USENIX Association, Boston, MA, 1415\u20131432. https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/nguyen"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.001.2100173"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2022.11.142"},{"key":"e_1_3_1_34_2","first-page":"21554","volume-title":"Advances in Neural Information Processing Systems","author":"Reisizadeh Amirhossein","year":"2020","unstructured":"Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, and Ali Jadbabaie. 2020. Robust federated learning: The case of affine distribution shifts. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., New Orleans, LA, 21554\u201321565. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/f5e536083a438cec5b64a4954abc17f1-Paper.pdf"},{"key":"e_1_3_1_35_2","first-page":"508","volume-title":"Proceedings of the 32nd Annual Conference on Computer Security Applications, ACSAC 2016, Los Angeles, CA, USA, December 5\u20139, 2016","author":"Shen Shiqi","year":"2016","unstructured":"Shiqi Shen, Shruti Tople, and Prateek Saxena. 2016. Auror: Defending against poisoning attacks in collaborative deep learning systems. In Proceedings of the 32nd Annual Conference on Computer Security Applications, ACSAC 2016, Los Angeles, CA, USA, December 5\u20139, 2016, Stephen Schwab, William K. Robertson, and Davide Balzarotti (Eds.). ACM, Los Angeles, CA, 508\u2013519. http:\/\/dl.acm.org\/citation.cfm?id=2991125"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3549939"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494322.3494326"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1911.07963"},{"key":"e_1_3_1_39_2","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"Dinh Canh T","year":"2020","unstructured":"Canh T Dinh, Nguyen Tran, and Josh Nguyen. 2020. Personalized federated learning with moreau envelopes. Advances in Neural Information Processing Systems 33 (2020), 21394\u201321405.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557439"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/EUROSP48549.2020.00019"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"e_1_3_1_45_2","first-page":"7611","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6\u201312, 2020, virtual","volume":"33","author":"Wang Jianyu","year":"2020","unstructured":"Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. 2020. Tackling the objective inconsistency problem in heterogeneous federated optimization. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6\u201312, 2020, virtual, Hugo Larochelle, Marc\u2019Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.), Vol. 33. Neural Information Processing Systems Foundation, Virtual Event, 7611\u20137623. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/564127c03caab942e503ee6f810f54fd-Abstract.html"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2306.08393"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3045266"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1708.07747"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2312.12484"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO54536.2021.9616052"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"e_1_3_1_52_2","first-page":"5650","volume-title":"International Conference on Machine Learning","author":"Yin Dong","year":"2018","unstructured":"Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning. PMLR, Stockholm, Sweden, 5650\u20135659."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/Confluence52989.2022.9734158"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599345"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.2986205"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-38991-8_39"}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690822","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:06Z","timestamp":1750294686000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,11,30]]}},"alternative-id":["10.1145\/3690822"],"URL":"https:\/\/doi.org\/10.1145\/3690822","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,15]]},"assertion":[{"value":"2024-01-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}