{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:43:53Z","timestamp":1753602233446,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"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":[],"published-print":{"date-parts":[[2023,10,6]]},"DOI":"10.1145\/3615593.3615720","type":"proceedings-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T10:04:40Z","timestamp":1710237880000},"page":"107-112","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["FedCOM"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0531-9802","authenticated-orcid":false,"given":"Xintong","family":"Lu","sequence":"first","affiliation":[{"name":"BUPT, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0135-8915","authenticated-orcid":false,"given":"Yuchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"BUPT, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7409-3700","authenticated-orcid":false,"given":"Huan","family":"Zou","sequence":"additional","affiliation":[{"name":"BUPT, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0520-8105","authenticated-orcid":false,"given":"Yilei","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6418-8087","authenticated-orcid":false,"given":"Wendong","family":"Wang","sequence":"additional","affiliation":[{"name":"BUPT, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7013-0121","authenticated-orcid":false,"given":"Jon","family":"Crowcroft","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Aaditya Kumar Singh, and Sunav Choudhary","author":"Arivazhagan Manoj Ghuhan","year":"2019","unstructured":"Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1371\/journal.pone.0130140"},{"key":"e_1_3_2_1_3_1","volume-title":"Mohammad Mahdi Kamani, and Mehrdad Mahdavi","author":"Deng Yuyang","year":"2020","unstructured":"Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi. 2020. Adaptive personalized federated learning. arXiv preprint arXiv:2003.13461 (2020)."},{"key":"e_1_3_2_1_4_1","volume-title":"Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks. arXiv e-prints","author":"Draelos Rachel Lea","year":"2020","unstructured":"Rachel Lea Draelos and Lawrence Carin. 2020. Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks. arXiv e-prints (2020), arXiv-2011."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1109\/TPDS.2020.3009406"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/TPDS.2020.3009406"},{"key":"e_1_3_2_1_7_1","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume":"33","author":"Ghosh Avishek","year":"2020","unstructured":"Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. 2020. An efficient framework for clustered federated learning. Advances in Neural Information Processing Systems 33 (2020), 19586--19597.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","first-page":"2304","article-title":"Lower bounds and optimal algorithms for personalized federated learning","volume":"33","author":"Hanzely Filip","year":"2020","unstructured":"Filip Hanzely, Slavom\u00edr Hanzely, Samuel Horv\u00e1th, and Peter Richt\u00e1rik. 2020. Lower bounds and optimal algorithms for personalized federated learning. Advances in Neural Information Processing Systems 33 (2020), 2304--2315.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_11_1","volume-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479","author":"Jeong Eunjeong","year":"2018","unstructured":"Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2018. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479 (2018)."},{"unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009).","key":"e_1_3_2_1_12_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Machine Learning. PMLR, 6357--6368","author":"Li Tian","year":"2021","unstructured":"Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021. Ditto: Fair and robust federated learning through personalization. In International Conference on Machine Learning. PMLR, 6357--6368."},{"key":"e_1_3_2_1_15_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_1_16_1","volume-title":"Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619","author":"Mansour Yishay","year":"2020","unstructured":"Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 (2020)."},{"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 1273--1282.","key":"e_1_3_2_1_17_1"},{"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 1273--1282.","key":"e_1_3_2_1_18_1"},{"volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 983--991","author":"Guruprasad Harish","unstructured":"Harish Guruprasad Ramaswamy et al. 2020. Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 983--991.","key":"e_1_3_2_1_19_1"},{"key":"e_1_3_2_1_20_1","volume-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","author":"Sattler Felix","year":"2020","unstructured":"Felix Sattler, Klaus-Robert M\u00fcller, and Wojciech Samek. 2020. Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints. IEEE transactions on neural networks and learning systems 32, 8 (2020), 3710--3722."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_1_22_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"e_1_3_2_1_24_1","volume-title":"Federated evaluation of on-device personalization. arXiv preprint arXiv:1910.10252","author":"Wang Kangkang","year":"2019","unstructured":"Kangkang Wang, Rajiv Mathews, Chlo\u00e9 Kiddon, Hubert Eichner, Fran\u00e7oise Beaufays, and Daniel Ramage. 2019. Federated evaluation of on-device personalization. arXiv preprint arXiv:1910.10252 (2019)."},{"key":"e_1_3_2_1_25_1","volume-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747","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. arXiv preprint arXiv:1708.07747 (2017)."},{"key":"e_1_3_2_1_26_1","volume-title":"Personalized Federated Learning with Feature Alignment and Classifier Collaboration. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SXZr8aDKia","author":"Xu Jian","year":"2023","unstructured":"Jian Xu, Xinyi Tong, and Shao-Lun Huang. 2023. Personalized Federated Learning with Feature Alignment and Classifier Collaboration. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SXZr8aDKia"},{"key":"e_1_3_2_1_27_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)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1109\/CVPR.2016.319"}],"event":{"sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"],"acronym":"ACM MobiCom '23","name":"ACM MobiCom '23: The 29th Annual International Conference on Mobile Computing and Networking","location":"Madrid Spain"},"container-title":["Proceedings of the 2nd ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615593.3615720","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3615593.3615720","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:10:17Z","timestamp":1750295417000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3615593.3615720"}},"subtitle":["Efficient Personalized Federated Learning by Finding Your Best Peers"],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":28,"alternative-id":["10.1145\/3615593.3615720","10.1145\/3615593"],"URL":"https:\/\/doi.org\/10.1145\/3615593.3615720","relation":{},"subject":[],"published":{"date-parts":[[2023,10,6]]},"assertion":[{"value":"2024-03-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}