{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:41:14Z","timestamp":1773931274708,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Research Foundation of Korea, Ministry of Science and ICT","award":["2022R1A3B1077720"],"award-info":[{"award-number":["2022R1A3B1077720"]}]},{"name":"BK21 FOUR program of the Education and Research Program for Future ICT Pioneers"},{"name":"LG Innotek"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,29]]},"DOI":"10.1145\/3545008.3545073","type":"proceedings-article","created":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T01:04:08Z","timestamp":1673744648000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":42,"title":["FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks"],"prefix":"10.1145","author":[{"given":"Jaehee","family":"Jang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea"}]},{"given":"Heoneok","family":"Ha","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea"}]},{"given":"Dahuin","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea"}]},{"given":"Sungroh","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea and AIIS, ASRI, and INMC, Seoul National University, Republic of Korea"}]}],"member":"320","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning (ICML). PMLR, 1597\u20131607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In International Conference on Machine Learning (ICML). PMLR, 1597\u20131607."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_2_1_3_1","volume-title":"EMNIST: Extending MNIST to Handwritten Letters. In 2017 international joint conference on neural networks (IJCNN)","author":"Cohen Gregory","year":"2017","unstructured":"Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van\u00a0Schaik. 2017. EMNIST: Extending MNIST to Handwritten Letters. In 2017 international joint conference on neural networks (IJCNN). IEEE, 2921\u20132926."},{"key":"e_1_3_2_1_4_1","unstructured":"Kedar Dhamdhere Mukund Sundararajan and Qiqi Yan. 2018. How Important is a Neuron?arXiv preprint arXiv:1805.12233(2018)."},{"key":"e_1_3_2_1_5_1","volume-title":"HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In International Conference on Learning Representations (ICLR).","author":"Diao Enmao","year":"2021","unstructured":"Enmao Diao, Jie Ding, and Vahid Tarokh. 2021. HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_6_1","unstructured":"Jean-Bastien Grill Florian Strub Florent Altch\u00e9 Corentin Tallec Pierre Richemond Elena Buchatskaya Carl Doersch Bernardo Pires Zhaohan Guo Mohammad Azar 2020. Bootstrap Your Own Latent: A new approach to self-supervised learning. In Neural Information Processing Systems."},{"key":"e_1_3_2_1_7_1","volume-title":"Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning. In International Conference on Learning Representations (ICLR).","author":"Gunel Beliz","year":"2020","unstructured":"Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. 2020. Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_8_1","first-page":"14068","article-title":"Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge","volume":"33","author":"He Chaoyang","year":"2020","unstructured":"Chaoyang He, Murali Annavaram, and Salman Avestimehr. 2020. Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. Advances in Neural Information Processing Systems 33 (2020), 14068\u201314080.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_11_1","unstructured":"Shaoxiong Ji Teemu Saravirta Shirui Pan Guodong Long and Anwar Walid. 2021. Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning. arXiv preprint arXiv:2102.12920(2021)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Peter Kairouz H\u00a0Brendan McMahan Brendan Avent Aur\u00e9lien Bellet Mehdi Bennis Arjun\u00a0Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings 2021. Advances and Open Problems in Federated Learning. Foundations and Trends\u00ae in Machine Learning 14 1\u20132(2021) 1\u2013210.","DOI":"10.1561\/2200000083"},{"key":"e_1_3_2_1_13_1","first-page":"18661","article-title":"Supervised Contrastive Learning","volume":"33","author":"Khosla Prannay","year":"2020","unstructured":"Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised Contrastive Learning. Advances in Neural Information Processing Systems 33 (2020), 18661\u201318673.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_14_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning Multiple Layers of Features from Tiny Images. (2009)."},{"key":"e_1_3_2_1_15_1","volume-title":"Advances in Neural Information Processing Systems, F.\u00a0Pereira, C.J. Burges, L.\u00a0Bottou, and K","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey\u00a0E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, F.\u00a0Pereira, C.J. Burges, L.\u00a0Bottou, and K.Q. Weinberger (Eds.). Vol.\u00a025."},{"key":"e_1_3_2_1_16_1","volume-title":"Survey of Personalization Techniques for Federated Learning. In 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). IEEE, 794\u2013797","author":"Kulkarni Viraj","year":"2020","unstructured":"Viraj Kulkarni, Milind Kulkarni, and Aniruddha Pant. 2020. Survey of Personalization Techniques for Federated Learning. In 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). IEEE, 794\u2013797."},{"key":"e_1_3_2_1_17_1","unstructured":"Daliang Li and Junpu Wang. 2019. FedMD: Heterogenous Federated Learning via Model Distillation. arXiv preprint arXiv:1910.03581(2019)."},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of Machine Learning and Systems, I.\u00a0Dhillon, D.\u00a0Papailiopoulos, and V.\u00a0Sze (Eds.). Vol.\u00a02. 429\u2013450","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit\u00a0Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. In Proceedings of Machine Learning and Systems, I.\u00a0Dhillon, D.\u00a0Papailiopoulos, and V.\u00a0Sze (Eds.). Vol.\u00a02. 429\u2013450."},{"key":"e_1_3_2_1_19_1","first-page":"2351","article-title":"Ensemble Distillation for Robust Model Fusion in Federated Learning","volume":"33","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Lingjing Kong, Sebastian\u00a0U Stich, and Martin Jaggi. 2020. Ensemble Distillation for Robust Model Fusion in Federated Learning. Advances in Neural Information Processing Systems 33 (2020), 2351\u20132363.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_2_1_21_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics (AISTATS). PMLR 1273\u20131282."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"e_1_3_2_1_24_1","volume-title":"The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI)","author":"Tan Yue","year":"2021","unstructured":"Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. 2021. FedProto: Federated prototype learning over heterogeneous devices. The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) (2021)."},{"key":"e_1_3_2_1_25_1","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Maaten Laurens Van\u00a0der","year":"2008","unstructured":"Laurens Van\u00a0der 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_1_26_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:cs.LG\/1708.07747\u00a0[cs.LG]"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467309"},{"key":"e_1_3_2_1_28_1","volume-title":"Parameterized Knowledge Transfer for Personalized Federated Learning. Advances in Neural Information Processing Systems 34","author":"Zhang Jie","year":"2021","unstructured":"Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wenchao Xu, and Feijie Wu. 2021. Parameterized Knowledge Transfer for Personalized Federated Learning. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_29_1","unstructured":"Lan Zhang and Xiaoyong Yuan. 2021. FedZKT: Zero-Shot Knowledge Transfer towards Heterogeneous On-Device Models in Federated Learning. arXiv preprint arXiv:2109.03775(2021)."}],"event":{"name":"ICPP '22: 51st International Conference on Parallel Processing","location":"Bordeaux France","acronym":"ICPP '22"},"container-title":["Proceedings of the 51st International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545008.3545073","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3545008.3545073","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:44Z","timestamp":1750186964000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545008.3545073"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":29,"alternative-id":["10.1145\/3545008.3545073","10.1145\/3545008"],"URL":"https:\/\/doi.org\/10.1145\/3545008.3545073","relation":{},"subject":[],"published":{"date-parts":[[2022,8,29]]},"assertion":[{"value":"2023-01-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}