{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T21:18:30Z","timestamp":1781039910229,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the European Research Council (ERC) via the REDIAL project (Grant Agreement ID: 805194)","award":["805194"],"award-info":[{"award-number":["805194"]}]},{"name":"ERC Project 833296 (EAR)","award":["833296"],"award-info":[{"award-number":["833296"]}]},{"name":"the UK?s Engineering and Physical Sciences Research Council (EPSRC) with grants EP\/M50659X\/1 and EP\/S001530\/1 (the MOA project)","award":["EP\/M50659X\/1 and EP\/S001530\/1"],"award-info":[{"award-number":["EP\/M50659X\/1 and EP\/S001530\/1"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671899","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"3484-3494","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6994-6318","authenticated-orcid":false,"given":"Tong","family":"Xia","sequence":"first","affiliation":[{"name":"University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4044-8523","authenticated-orcid":false,"given":"Abhirup","family":"Ghosh","sequence":"additional","affiliation":[{"name":"University of Birmingham & University of Cambridge, Birmingham, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7654-4448","authenticated-orcid":false,"given":"Xinchi","family":"Qiu","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-4380","authenticated-orcid":false,"given":"Cecilia","family":"Mascolo","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_2_2_1","volume-title":"On large-cohort training for federated learning. Advances in neural information processing systems 34","author":"Charles Zachary","year":"2021","unstructured":"Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith. 2021. On large-cohort training for federated learning. Advances in neural information processing systems 34 (2021), 20461--20475."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.522"},{"key":"e_1_3_2_2_5_1","unstructured":"Long Pham Duc. 2023. Sound classification . Kaggle. https:\/\/www.kaggle.com\/ code\/longx99\/sound-classification\/notebook."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/fi13040094"},{"key":"e_1_3_2_2_7_1","unstructured":"Yongxin Guo Xiaoying Tang and Tao Lin. 2023. FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. (2023)."},{"key":"e_1_3_2_2_8_1","volume-title":"Handbook of beta distribution and its applications","author":"Gupta Arjun K","unstructured":"Arjun K Gupta and Saralees Nadarajah. 2004. Handbook of beta distribution and its applications. CRC press."},{"key":"e_1_3_2_2_9_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Machine Learning. PMLR, 5132--5143","author":"Karimireddy Sai Praneeth","year":"2020","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning. PMLR, 5132--5143."},{"key":"e_1_3_2_2_11_1","volume-title":"Cifar-10 and cifar-100 datasets. URL: https:\/\/www.cs.toronto.edu\/ kriz\/ cifar.html","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. 2009. Cifar-10 and cifar-100 datasets. URL: https:\/\/www.cs.toronto.edu\/ kriz\/ cifar.html (2009)."},{"key":"e_1_3_2_2_12_1","volume-title":"International Conference on Machine Learning. PMLR, 11814--11827","author":"Lai Fan","year":"2022","unstructured":"Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, and Mosharaf Chowdhury. 2022. Fedscale: Benchmarking model and system performance of federated learning at scale. In International Conference on Machine Learning. PMLR, 11814--11827."},{"key":"e_1_3_2_2_13_1","unstructured":"Gihun Lee Minchan Jeong Yongjin Shin Sangmin Bae and Se-Young Yun. 2022. Preservation of the global knowledge by not-true distillation in federated learning. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of Machine learning and systems 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--450."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467254"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000215"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05933-9_48"},{"key":"e_1_3_2_2_21_1","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-iid data","volume":"34","author":"Luo Mi","year":"2021","unstructured":"Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng. 2021. No fear of heterogeneity: Classifier calibration for federated learning with non-iid data. Advances in Neural Information Processing Systems 34 (2021), 5972--5984.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_22_1","volume-title":"Proc. Artificial Intelligence and Statistics. 1273--1282","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 Proc. Artificial Intelligence and Statistics. 1273--1282."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","unstructured":"Jorge Reyes-Ortiz Davide Anguita Alessandro Ghio Luca Oneto and Xavier Parra. 2012. Human Activity Recognition Using Smartphones. UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C54S4K.","DOI":"10.24432\/C54S4K"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2655045"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_2_26_1","volume-title":"Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. In The Eleventh International Conference on Learning Representations.","author":"Shi Yujun","year":"2022","unstructured":"Yujun Shi, Jian Liang,Wenqing Zhang, Vincent Tan, and Song Bai. 2022. Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"G\u00e1bor J Sz\u00e9kely Maria L Rizzo and Nail K Bakirov. 2007. Measuring and testing dependence by correlation of distances. (2007).","DOI":"10.1214\/009053607000000505"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"e_1_3_2_2_29_1","volume-title":"International Conference on Machine Learning. PMLR, 21111--21132","author":"Tang Zhenheng","year":"2022","unstructured":"Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, and Xiaowen Chu. 2022. Virtual homogeneity learning: Defending against data heterogeneity in federated learning. In International Conference on Machine Learning. PMLR, 21111--21132."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.2978772"},{"key":"e_1_3_2_2_31_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW51313.2020.00134"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Praneeth Vepakomma Chetan Tonde and Ahmed Elgammal. 2018. Supervised dimensionality reduction via distance correlation maximization. (2018).","DOI":"10.1214\/18-EJS1403"},{"key":"e_1_3_2_2_34_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems.","author":"Yang Zhiqin","year":"2023","unstructured":"Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, and Bo Han. 2023. FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_35_1","volume-title":"International Conference on Learning Representations.","author":"Yoon Tehrim","year":"2020","unstructured":"Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang. 2020. FedMix: Approximation of Mixup under Mean Augmented Federated Learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_36_1","volume-title":"International conference on machine learning. PMLR, 7252--7261","author":"Yurochkin Mikhail","year":"2019","unstructured":"Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, and Yasaman Khazaeni. 2019. Bayesian nonparametric federated learning of neural networks. In International conference on machine learning. PMLR, 7252--7261."},{"key":"e_1_3_2_2_37_1","volume-title":"Mixup: Beyond Empirical Risk Minimization. In International Conference on Learning Representations.","author":"Zhang Hongyi","year":"2018","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2018. Mixup: Beyond Empirical Risk Minimization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Machine Learning. PMLR, 26311--26329","author":"Zhang Jie","year":"2022","unstructured":"Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, and Chao Wu. 2022. Federated learning with label distribution skew via logits calibration. In International Conference on Machine Learning. PMLR, 26311--26329."},{"key":"e_1_3_2_2_39_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_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02351"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671899","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671899","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:15Z","timestamp":1750291455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671899"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":40,"alternative-id":["10.1145\/3637528.3671899","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671899","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}