{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:22:04Z","timestamp":1774682524997,"version":"3.50.1"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372307, U2001207, U2003206, and U20B2048"],"award-info":[{"award-number":["62372307, U2001207, U2003206, and U20B2048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fundamental Research Funds"},{"name":"Central Universities","award":["3072024XX0603"],"award-info":[{"award-number":["3072024XX0603"]}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Natural Science Foundation","doi-asserted-by":"crossref","award":["2024A1515011691"],"award-info":[{"award-number":["2024A1515011691"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Science and Technology Program","award":["RCYX20231211090129039"],"award-info":[{"award-number":["RCYX20231211090129039"]}]},{"name":"Shenzhen Science and Technology Foundation","award":["JCYJ20230808105906014"],"award-info":[{"award-number":["JCYJ20230808105906014"]}]},{"name":"Guangdong Provincial Key Laboratory of Integrated Communication, Sensing, and Computation for Ubiquitous Internet of Things","award":["2023B1212010007"],"award-info":[{"award-number":["2023B1212010007"]}]},{"name":"Project of DEGP","award":["2023KCXTD042"],"award-info":[{"award-number":["2023KCXTD042"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>Federated learning (FL) has become a prominent paradigm for collaborative model training while ensuring data privacy. However, in resource-constrained environments, such as the Internet of Things (IoT), FL faces a distinct challenge from Lazybone attackers, who compromise system performance by providing low-quality data or conducting minimal local training to reduce their computational burden. In this article, we propose Fedeval, a novel multi-dimensional evaluation framework designed to defend against Lazybone attacks. Fedeval leverages a server-side base validation dataset and a base model to assess the quality and relevance of client contributions through gradient inversion, and it compares client-uploaded gradients with an honest baseline to detect training inconsistencies. By assigning adaptive importance scores based on client contributions, Fedeval enhances the robustness of FL by mitigating the impact of non-contributing participants. We also provide a theoretical analysis of Fedeval\u2019s convergence properties and validate its effectiveness through extensive experiments on four datasets and two attack scenarios. Our results demonstrate that Fedeval significantly accelerates convergence and improves accuracy by up to 13% compared to traditional methods.<\/jats:p>","DOI":"10.1145\/3703631","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:58:07Z","timestamp":1733309887000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9191-602X","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0081-7914","authenticated-orcid":false,"given":"Haoran","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6345-3873","authenticated-orcid":false,"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen University College of Computer Science &amp; Software Engineering, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0332-0686","authenticated-orcid":false,"given":"Shichang","family":"Xuan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9205-1881","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China and Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1273","volume-title":"Proceedings of the 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 Proceedings of the Artificial Intelligence and Statistics. PMLR, 1273\u20131282."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.003.2100028"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759317"},{"key":"e_1_3_1_9_2","unstructured":"Priyanka Mary Mammen. 2021. Federated Learning: Opportunities and Challenges. arXiv e-prints (2021): arXiv-2101."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_17"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109490"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3063147"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460664"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2019.2897554"},{"key":"e_1_3_1_16_2","unstructured":"P. Blanchard E. M. El Mhamdi R. Guerraoui et\u00a0al. 2017. Machine learning with adversaries: byzantine tolerant gradient descent. Proceedings of the 31st International Conference on Neural Information Processing Systems. 118\u2013128."},{"key":"e_1_3_1_17_2","unstructured":"Suyi Li et\u00a0al. 2019. Abnormal Client Behavior Detection in Federated Learning. arXiv e-prints (2019): arXiv-1910."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.037"},{"key":"e_1_3_1_19_2","first-page":"5739","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Shen Yanyao","year":"2019","unstructured":"Yanyao Shen and Sujay Sanghavi. 2019. Learning with bad training data via iterative trimmed loss minimization. In Proceedings of the International Conference on Machine Learning. PMLR, 5739\u20135748."},{"key":"e_1_3_1_20_2","unstructured":"Battista Biggio Blaine Nelson and Pavel Laskov. 2012. Poisoning attacks against support vector machines. Proceedings of the 29th International Coference on International Conference on Machine Learning."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9891990"},{"key":"e_1_3_1_23_2","unstructured":"A. Shafahi W. R. Huang M. Najibi et\u00a0al. 2018. Poison frogs! targeted clean-label poisoning attacks on neural networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems. 6106\u20136116."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3023126"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi13030073"},{"key":"e_1_3_1_26_2","unstructured":"Xiaoyu Cao et\u00a0al. 2021. FLTrust: Byzantine-robust federated learning via trust bootstrapping. ISOC Network and Distributed System Security Symposium (NDSS\u201921)."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118347"},{"key":"e_1_3_1_28_2","unstructured":"Clement Fung Chris JM Yoon and Ivan Beschastnikh. 2018. Mitigating Sybils in Federated Learning Poisoning. arXiv eprints (2018): arXiv-1808."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"e_1_3_1_30_2","unstructured":"Ziteng Sun et\u00a0al. 2019. Can You Really Backdoor Federated Learning? arXiv e-prints (2019): arXiv-1911."},{"key":"e_1_3_1_31_2","first-page":"7587","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics","author":"Panda Ashwinee","year":"2022","unstructured":"Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, and Prateek Mittal. 2022. Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification. In Proceedings of the International Conference on Artificial Intelligence and Statistics. PMLR, 7587\u20137624."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.01.1900525"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3031503"},{"key":"e_1_3_1_34_2","first-page":"2525","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research)","volume":"80","author":"Katharopoulos Angelos","year":"2018","unstructured":"Angelos Katharopoulos and Francois Fleuret. 2018. Not All samples are created equal: Deep learning with importance sampling. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research), Jennifer Dy and Andreas Krause (Eds.). Vol. 80, PMLR, 2525\u20132534. Retrieved from https:\/\/proceedings.mlr.press\/v80\/katharopoulos18a.html"},{"key":"e_1_3_1_35_2","first-page":"10351","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics","author":"Cho Yae Jee","year":"2022","unstructured":"Yae Jee Cho, Jianyu Wang, and Gauri Joshi. 2022. Towards understanding biased client selection in federated learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics. PMLR, 10351\u201310375."},{"key":"e_1_3_1_36_2","first-page":"629","volume-title":"Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation","author":"Hsieh Kevin","year":"2017","unstructured":"Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, Phillip B Gibbons, and Onur Mutlu. 2017. Gaia: \\(\\lbrace\\) geo-distributed \\(\\rbrace\\) machine learning approaching \\(\\lbrace\\) LAN \\(\\rbrace\\) speeds. In Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation. 629\u2013647."},{"issue":"2","key":"e_1_3_1_37_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3534585","article-title":"Are you left out? An efficient and fair federated learning for personalized profiles on wearable devices of inferior networking conditions","volume":"6","author":"Zhou Pengyuan","year":"2022","unstructured":"Pengyuan Zhou, Hengwei Xu, Lik Hang Lee, Pei Fang, and Pan Hui. 2022. Are you left out? An efficient and fair federated learning for personalized profiles on wearable devices of inferior networking conditions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1\u201325.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"e_1_3_1_39_2","unstructured":"A. Krizhevsky. 2009. Learning Multiple Layers of Features from Tiny Images. Master\u2019s thesis University of Tront (2009)."},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_1_41_2","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv e-prints (2017): arXiv-1708."}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3703631","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3703631","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:09:42Z","timestamp":1750295382000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3703631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3703631"],"URL":"https:\/\/doi.org\/10.1145\/3703631","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"2023-11-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}