{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T13:54:02Z","timestamp":1784555642744,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":73,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB2900102"],"award-info":[{"award-number":["2021YFB2900102"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62072436"],"award-info":[{"award-number":["No. 62072436"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679550","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"1015-1026","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Tackling Noisy Clients in Federated Learning with End-to-end Label Correction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0211-9123","authenticated-orcid":false,"given":"Xuefeng","family":"Jiang","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0260-2692","authenticated-orcid":false,"given":"Sheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3987-5034","authenticated-orcid":false,"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8328-3015","authenticated-orcid":false,"given":"Jingjing","family":"Xue","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8279-4750","authenticated-orcid":false,"given":"Runhan","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8925-4896","authenticated-orcid":false,"given":"Zhiyuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3737-6477","authenticated-orcid":false,"given":"Gang","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3228-7371","authenticated-orcid":false,"given":"Yuwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2824-9601","authenticated-orcid":false,"given":"Min","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences & Zhongguancun Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017","author":"Arpit Devansh","year":"2017","unstructured":"Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste-Julien. 2017. A Closer Look at Memorization in Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6--11 August 2017 (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 233--242. http:\/\/proceedings.mlr.press\/v70\/arpit17a.html"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00637"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_4_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Berthelot David","year":"2019","unstructured":"David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019. MixMatch: A Holistic Approach to Semi-Supervised Learning. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada,, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.). 5050--5060. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/1cd138d0499a68f4bb72bee04bbec2d7-Abstract.html"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2015974"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18","volume":"1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607. http:\/\/proceedings.mlr.press\/v119\/chen20j.html"},{"key":"e_1_3_2_1_7_1","volume-title":"Focus: Dealing with label quality disparity in federated learning. arXiv preprint arXiv:2001.11359","author":"Chen Yiqiang","year":"2020","unstructured":"Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, and Zhiqi Shen. 2020. Focus: Dealing with label quality disparity in federated learning. arXiv preprint arXiv:2001.11359 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Bucila Cristian R Caruana and A Niculescu-Mizil. 2006. Model compression. In ACM SIGKDD.","DOI":"10.1145\/1150402.1150464"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"e_1_3_2_1_10_1","volume-title":"A continual learning survey: Defying forgetting in classification tasks","author":"Delange Matthias","year":"2021","unstructured":"Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars. 2021. A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3009406"},{"key":"e_1_3_2_1_13_1","volume-title":"NIPS 2004","author":"Grandvalet Yves","year":"2004","unstructured":"Yves Grandvalet and Yoshua Bengio. 2004. Semi-supervised Learning by Entropy Minimization. In Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13--18, 2004, Vancouver, British Columbia, Canada]. 529--536. https:\/\/proceedings.neurips.cc\/paper\/2004\/hash\/96f2b50b5d3613adf9c27049b2a888c7-Abstract.html"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.26615\/978-954-452-056-4_050"},{"key":"e_1_3_2_1_15_1","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Han Bo","year":"2018","unstructured":"Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama. 2018. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 8536--8546. https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/a19744e268754fb0148b017647355b7b-Abstract.html"},{"key":"e_1_3_2_1_16_1","volume-title":"Zhenheng Tang, Di Fan, Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, and Salman Avestimehr.","author":"He Chaoyang","year":"2021","unstructured":"Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan, Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, and Salman Avestimehr. 2021. FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks. CoRR, Vol. abs\/2111.11066 (2021). showeprint[arXiv]2111.11066 https:\/\/arxiv.org\/abs\/2111.11066"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_19_1","volume-title":"Distilling the Knowledge in a Neural Network. CoRR","author":"Hinton Geoffrey E.","year":"2015","unstructured":"Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. CoRR, Vol. abs\/1503.02531 (2015). showeprint[arXiv]1503.02531 http:\/\/arxiv.org\/abs\/1503.02531"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00110"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3-030--58607--2_5"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-020-00780--4"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan","author":"Jiang Lu","year":"2018","unstructured":"Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. 2018. MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10--15, 2018 (Proceedings of Machine Learning Research, Vol. 80),, Jennifer G. Dy and Andreas Krause (Eds.). PMLR, 2309--2318. http:\/\/proceedings.mlr.press\/v80\/jiang18c.html"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18","volume":"5143","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 Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 5132--5143. http:\/\/proceedings.mlr.press\/v119\/karimireddy20a.html"},{"key":"e_1_3_2_1_25_1","volume-title":"Ananda Theertha Suresh, and Dave Bacon","author":"Konevcn\u00fd Jakub","year":"2016","unstructured":"Jakub Konevcn\u00fd, H. Brendan McMahan, Felix X. Yu, Peter Richt\u00e1rik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. CoRR, Vol. abs\/1610.05492 (2016). showeprint[arXiv]1610.05492 http:\/\/arxiv.org\/abs\/1610.05492"},{"key":"e_1_3_2_1_26_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_27_1","volume-title":"Hinton","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3--6, 2012, Lake Tahoe, Nevada, United States,, Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, L\u00e9on Bottou, and Kilian Q. Weinberger (Eds.). 1106--1114. https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"key":"e_1_3_2_1_28_1","volume-title":"The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale. CoRR","author":"Kuznetsova Alina","year":"2018","unstructured":"Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper R. R. Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari. 2018. The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale. CoRR, Vol. abs\/1811.00982 (2018). showeprint[arXiv]1811.00982 http:\/\/arxiv.org\/abs\/1811.00982"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"e_1_3_2_1_30_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Laine Samuli","year":"2017","unstructured":"Samuli Laine and Timo Aila. 2017. Temporal Ensembling for Semi-Supervised Learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=BJ6oOfqge"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_32_1","unstructured":"Daliang Li and Junpu Wang. 2019. FedMD: Heterogenous Federated Learning via Model Distillation. CoRR Vol. abs\/1910.03581 (2019). showeprint[arXiv]1910.03581 http:\/\/arxiv.org\/abs\/1910.03581"},{"key":"e_1_3_2_1_33_1","volume-title":"8th International Conference on Learning Representations, ICLR 2020","author":"Li Junnan","year":"2020","unstructured":"Junnan Li, Richard Socher, and Steven C. H. Hoi. 2020. DivideMix: Learning with Noisy Labels as Semi-supervised Learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=HJgExaVtwr"},{"key":"e_1_3_2_1_34_1","volume-title":"MoPro: Webly Supervised Learning with Momentum Prototypes. In 9th International Conference on Learning Representations, ICLR 2021","author":"Li Junnan","year":"2021","unstructured":"Junnan Li, Caiming Xiong, and Steven C. H. Hoi. 2021. MoPro: Webly Supervised Learning with Momentum Prototypes. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=0-EYBhgw80y"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"e_1_3_2_1_36_1","volume-title":"On the Convergence of FedAvg on Non-IID Data. In 8th International Conference on Learning Representations, ICLR 2020","author":"Li Xiang","year":"2020","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2020. On the Convergence of FedAvg on Non-IID Data. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=HJxNAnVtDS"},{"key":"e_1_3_2_1_37_1","volume-title":"Early-Learning Regularization Prevents Memorization of Noisy Labels. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Liu Sheng","year":"2020","unstructured":"Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. 2020. Early-Learning Regularization Prevents Memorization of Noisy Labels. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual,, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/ea89621bee7c88b2c5be6681c8ef4906-Abstract.html"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3-030-01216--8_12"},{"key":"e_1_3_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Malach Eran","year":"2017","unstructured":"Eran Malach and Shai Shalev-Shwartz. 2017. Decoupling \"when to update\" from \"how to update\". In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA,, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 960--970. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/58d4d1e7b1e97b258c9ed0b37e02d087-Abstract.html"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20--22","volume":"1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20--22 April 2017, Fort Lauderdale, FL, USA (Proceedings of Machine Learning Research, Vol. 54), Aarti Singh and Xiaojin (Jerry) Zhu (Eds.). PMLR, 1273--1282. http:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"e_1_3_2_1_42_1","volume-title":"Mixed Precision Training. In International Conference on Learning Representations.","author":"Micikevicius Paulius","year":"2018","unstructured":"Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al. 2018. Mixed Precision Training. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_43_1","volume-title":"Self-Distillation Amplifies Regularization in Hilbert Space. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Mobahi Hossein","year":"2020","unstructured":"Hossein Mobahi, Mehrdad Farajtabar, and Peter L. Bartlett. 2020. Self-Distillation Amplifies Regularization in Hilbert Space. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual,, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/2288f691b58edecadcc9a8691762b4fd-Abstract.html"},{"key":"e_1_3_2_1_44_1","volume-title":"Learning with noisy labels. Advances in neural information processing systems","author":"Natarajan Nagarajan","year":"2013","unstructured":"Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. 2013. Learning with noisy labels. Advances in neural information processing systems, Vol. 26 (2013)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00138"},{"key":"e_1_3_2_1_46_1","volume-title":"Augmentation Strategies for Learning With Noisy Labels. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021","author":"Nishi Kento","year":"2021","unstructured":"Kento Nishi, Yi Ding, Alex Rich, and Tobias H\u00f6llerer. 2021. Augmentation Strategies for Learning With Noisy Labels. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19--25, 2021. Computer Vision Foundation \/ IEEE, 8022--8031. https:\/\/openaccess.thecvf.com\/content\/CVPR2021\/html\/Nishi_Augmentation_Strategies_for_Learning_With_Noisy_Labels_CVPR_2021_paper.html"},{"key":"e_1_3_2_1_47_1","volume-title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).","author":"Northcutt Curtis G","year":"2021","unstructured":"Curtis G Northcutt, Anish Athalye, and Jonas Mueller. 2021. Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)."},{"key":"e_1_3_2_1_48_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"e_1_3_2_1_50_1","volume-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. IEEE transactions on neural networks and learning systems, Vol. 31, 9 (2019), 3400--3413."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3054610"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"e_1_3_2_1_53_1","volume-title":"Changchang Liu, and Kin K. Leung.","author":"Tuor Tiffany","year":"2020","unstructured":"Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, and Kin K. Leung. 2020. Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients. CoRR, Vol. abs\/2001.08300 (2020). showeprint[arXiv]2001.08300 https:\/\/arxiv.org\/abs\/2001.08300"},{"key":"e_1_3_2_1_54_1","volume-title":"Learning with Symmetric Label Noise: The Importance of Being Unhinged. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015","author":"van Rooyen Brendan","year":"2015","unstructured":"Brendan van Rooyen, Aditya Krishna Menon, and Robert C. Williamson. 2015. Learning with Symmetric Label Noise: The Importance of Being Unhinged. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7--12, 2015, Montreal, Quebec, Canada,, Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett (Eds.). 10--18. https:\/\/proceedings.neurips.cc\/paper\/2015\/hash\/45c48cce2e2d7fbdea1afc51c7c6ad26-Abstract.html"},{"key":"e_1_3_2_1_55_1","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Wang Hongyi","year":"2018","unstructured":"Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, and Stephen J. Wright. 2018. ATOMO: Communication-efficient Learning via Atomic Sparsification. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 9872--9883. https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/33b3214d792caf311e1f00fd22b392c5-Abstract.html"},{"key":"e_1_3_2_1_56_1","volume-title":"Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks","author":"Wang Lin","year":"2021","unstructured":"Lin Wang and Kuk-Jin Yoon. 2021. Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_1_58_1","volume-title":"Vertical Federated Learning: Challenges, Methodologies and Experiments. arXiv preprint arXiv:2202.04309","author":"Wei Kang","year":"2022","unstructured":"Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, and Thilina Ranbaduge. 2022. Vertical Federated Learning: Challenges, Methodologies and Experiments. arXiv preprint arXiv:2202.04309 (2022)."},{"key":"e_1_3_2_1_59_1","volume-title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR","author":"Xiao Han","year":"2017","unstructured":"Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR, Vol. abs\/1708.07747 (2017). showeprint[arXiv]1708.07747 http:\/\/arxiv.org\/abs\/1708.07747"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"e_1_3_2_1_61_1","volume-title":"Asynchronous federated optimization. arXiv preprint arXiv:1903.03934","author":"Xie Cong","year":"2019","unstructured":"Cong Xie, Sanmi Koyejo, and Indranil Gupta. 2019. Asynchronous federated optimization. arXiv preprint arXiv:1903.03934 (2019)."},{"key":"e_1_3_2_1_62_1","volume-title":"Unsupervised Data Augmentation for Consistency Training. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Xie Qizhe","year":"2020","unstructured":"Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020. Unsupervised Data Augmentation for Consistency Training. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual,, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/44feb0096faa8326192570788b38c1d1-Abstract.html"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3131852"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2022.3151466"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"e_1_3_2_1_68_1","volume-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=Sy8gdB9xx","author":"Zhang Chiyuan","year":"2017","unstructured":"Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017. Understanding deep learning requires rethinking generalization. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=Sy8gdB9xx"},{"key":"e_1_3_2_1_69_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Zhang Hongyi","year":"2018","unstructured":"Hongyi Zhang, Moustapha Ciss\u00e9, Yann N. Dauphin, and David Lopez-Paz. 2018. mixup: Beyond Empirical Risk Minimization. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=r1Ddp1-Rb"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00381"},{"key":"e_1_3_2_1_71_1","volume-title":"Sabuncu","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert R. Sabuncu. 2018. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 8792--8802. https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/f2925f97bc13ad2852a7a551802feea0-Abstract.html"},{"key":"e_1_3_2_1_72_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","unstructured":"Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)."},{"key":"e_1_3_2_1_73_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Zhu Ligeng","year":"2019","unstructured":"Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep Leakage from Gradients. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada,, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett (Eds.). 14747--14756. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/60a6c4002cc7b29142def8871531281a-Abstract.html"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA","acronym":"CIKM '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679550","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:22Z","timestamp":1750294702000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":73,"alternative-id":["10.1145\/3627673.3679550","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679550","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}