{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:26:21Z","timestamp":1774365981074,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761368","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:18:04Z","timestamp":1762561084000},"page":"260-269","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["HyperGenFL: Hypernetwork-Generated Model Aggregation in Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3748-5940","authenticated-orcid":false,"given":"Jerry","family":"Chen","sequence":"first","affiliation":[{"name":"University of Alberta, Edmonton, AB, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8570-9761","authenticated-orcid":false,"given":"Qikai","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Alberta, Edmonton, AB, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5104-4208","authenticated-orcid":false,"given":"Ruiqing","family":"Tian","sequence":"additional","affiliation":[{"name":"University of Alberta, Edmonton, AB, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5250-7327","authenticated-orcid":false,"given":"Di","family":"Niu","sequence":"additional","affiliation":[{"name":"University of Alberta, Edmonton, AB, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2404-0974","authenticated-orcid":false,"given":"Baochun","family":"Li","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3250275"},{"key":"e_1_3_2_1_2_1","volume-title":"Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama.","author":"Emre Acar Durmus Alp","year":"2021","unstructured":"Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama. 2021. Federated learning based on dynamic regularization. arXiv preprint arXiv:2111.04263 (2021)."},{"key":"e_1_3_2_1_3_1","volume-title":"Fedbe: Making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974","author":"Chen Hong-You","year":"2020","unstructured":"Hong-You Chen and Wei-Lun Chao. 2020. Fedbe: Making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974 (2020)."},{"key":"e_1_3_2_1_4_1","volume-title":"International conference on machine learning. PMLR, 2611--2620","author":"Dennis Don Kurian","year":"2021","unstructured":"Don Kurian Dennis, Tian Li, and Virginia Smith. 2021. Heterogeneity for the win: One-shot federated clustering. In International conference on machine learning. PMLR, 2611--2620."},{"key":"e_1_3_2_1_5_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Diao Yiqun","year":"2023","unstructured":"Yiqun Diao, Qinbin Li, and Bingsheng He. 2023. Towards addressing label skews in one-shot federated learning. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_44"},{"key":"e_1_3_2_1_7_1","volume-title":"An efficient framework for clustered federated learning. Advances in neural information processing systems 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."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0186-1"},{"key":"e_1_3_2_1_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_1_11_1","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","DOI":"10.1109\/JSEN.2021.3076767"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_2_1_14_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_15_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Li Zexi","year":"2023","unstructured":"Zexi Li, Tao Lin, Xinyi Shang, and Chao Wu. 2023. Revisiting weighted aggregation in federated learning with neural networks. In International Conference on Machine Learning. PMLR, 19767--19788."},{"key":"e_1_3_2_1_16_1","volume-title":"Ensemble distillation for robust model fusion in federated learning. Advances in neural information processing systems 33","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020. Ensemble distillation for robust model fusion in federated learning. Advances in neural information processing systems 33 (2020), 2351--2363."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"},{"key":"e_1_3_2_1_18_1","volume-title":"FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability","author":"Lu Jianfeng","year":"2024","unstructured":"Jianfeng Lu, Hangjian Zhang, Pan Zhou, Xiong Wang, Chen Wang, and Dapeng Oliver Wu. 2024. FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability. IEEE Transactions on Emerging Topics in Computational Intelligence (2024)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611978032.94"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00985"},{"key":"e_1_3_2_1_21_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_22_1","unstructured":"mnmoustafa and Mohammed Ali. 2017. Tiny ImageNet. https:\/\/kaggle.com\/competitions\/tiny-imagenet. Kaggle."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20755"},{"key":"e_1_3_2_1_24_1","volume-title":"AIP Conference Proceedings","volume":"2909","author":"Nevrataki Theodora","year":"2023","unstructured":"Theodora Nevrataki, Anastasia Iliadou, George Ntolkeras, Ioannis Sfakianakis, Lazaros Lazaridis, George Maraslidis, Nikolaos Asimopoulos, and George F Fragulis. 2023. A survey on federated learning applications in healthcare, finance, and data privacy\/data security. In AIP Conference Proceedings, Vol. 2909. AIP Publishing."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3385440"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC58397.2023.10218040"},{"key":"e_1_3_2_1_28_1","volume-title":"International conference on machine learning. PMLR","author":"Qu Zhe","year":"2022","unstructured":"Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, and Zhuo Lu. 2022. Generalized federated learning via sharpness aware minimization. In International conference on machine learning. PMLR, 18250--18280."},{"key":"e_1_3_2_1_29_1","volume-title":"Rajnesh Singh, and Narendra Singh.","author":"Singh Pushpa","year":"2022","unstructured":"Pushpa Singh, Murari Kumar Singh, Rajnesh Singh, and Narendra Singh. 2022. Federated learning: Challenges, methods, and future directions. In Federated Learning for IoT Applications. Springer, 199--214."},{"key":"e_1_3_2_1_30_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in neural information processing systems 33","author":"Wang Jianyu","year":"2020","unstructured":"Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor. 2020. Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in neural information processing systems 33 (2020), 7611--7623."},{"key":"e_1_3_2_1_32_1","first-page":"19124","article-title":"A unified analysis of federated learning with arbitrary client participation","volume":"35","author":"Wang Shiqiang","year":"2022","unstructured":"Shiqiang Wang and Mingyue Ji. 2022. A unified analysis of federated learning with arbitrary client participation. Advances in Neural Information Processing Systems 35 (2022), 19124--19137.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_33_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_34_1","first-page":"10752","article-title":"On convergence of fedprox: Local dissimilarity invariant bounds, non-smoothness and beyond","volume":"35","author":"Yuan Xiaotong","year":"2022","unstructured":"Xiaotong Yuan and Ping Li. 2022. On convergence of fedprox: Local dissimilarity invariant bounds, non-smoothness and beyond. Advances in Neural Information Processing Systems 35 (2022), 10752--10765.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_35_1","unstructured":"Feilong Zhang Yinchuan Li Shiyi Lin Junjun Jiang Xianming Liu et al. 2023. Large sparse kernels for federated learning. (2023)."},{"key":"e_1_3_2_1_36_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_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413377"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","location":"Seoul Republic of Korea","acronym":"CIKM '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:40:07Z","timestamp":1765503607000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761368"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":37,"alternative-id":["10.1145\/3746252.3761368","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761368","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}