{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:16:39Z","timestamp":1783152999679,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":62,"publisher":"ACM","funder":[{"name":"National Key R&D Program of China","award":["2023YFB4502400"],"award-info":[{"award-number":["2023YFB4502400"]}]},{"name":"Hubei Provincial Science and Technology Innovation Base &#x28;Platform&#x29; Program Project","award":["2025CSA112"],"award-info":[{"award-number":["2025CSA112"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792140","type":"proceedings-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:54:34Z","timestamp":1775771674000},"page":"5798-5809","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Thorki: Decoupling General and Personalized Knowledge with Collaborative Fusion for Personalized Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-4556","authenticated-orcid":false,"given":"Qiang","family":"He","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China and Swinburne University of Technology, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8188-340X","authenticated-orcid":false,"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6877-2471","authenticated-orcid":false,"given":"Yicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"HuaZhong University of science and technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5040-1468","authenticated-orcid":false,"given":"Sheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0545-8187","authenticated-orcid":false,"given":"Haipeng","family":"Dai","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5455-3792","authenticated-orcid":false,"given":"Feifei","family":"Chen","sequence":"additional","affiliation":[{"name":"Deakin University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3934-7605","authenticated-orcid":false,"given":"Hai","family":"Jin","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7868-5471","authenticated-orcid":false,"given":"Yun","family":"Yang","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3592505"},{"key":"e_1_3_2_1_3_1","volume-title":"Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876","author":"Chen Fei","year":"2018","unstructured":"Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He. 2018. Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876 (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"On Bridging Generic and Personalized Federated Learning for Image Classification. In International Conference on Learning Representations.","author":"Chen Hong-You","year":"2021","unstructured":"Hong-You Chen and Wei-Lun Chao. 2021. On Bridging Generic and Personalized Federated Learning for Image Classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_5_1","volume-title":"International conference on machine learning. PMLR","author":"Collins Liam","year":"2021","unstructured":"Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. 2021. Exploiting shared representations for personalized federated learning. In International conference on machine learning. PMLR, 2089-2099."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25891"},{"key":"e_1_3_2_1_7_1","first-page":"4171","volume-title":"Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 4171-4186."},{"key":"e_1_3_2_1_8_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)."},{"key":"e_1_3_2_1_9_1","volume-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in neural information processing systems","author":"Fallah Alireza","year":"2020","unstructured":"Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in neural information processing systems, Vol. 33 (2020), 3557-3568."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835918"},{"key":"e_1_3_2_1_11_1","first-page":"705","volume-title":"18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21)","author":"Guo Peizhen","year":"2021","unstructured":"Peizhen Guo, Bo Hu, and Wenjun Hu. 2021. Mistify: Automating model porting for inference at the edge. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). 705-719."},{"key":"e_1_3_2_1_12_1","volume-title":"Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335","author":"Harry Hsu Tzu-Ming","year":"2019","unstructured":"Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019. Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335 (2019)."},{"key":"e_1_3_2_1_13_1","first-page":"3","article-title":"Lora: Low-rank adaptation of large language models","volume":"1","author":"Hu Edward J","year":"2022","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al., 2022. Lora: Low-rank adaptation of large language models. ICLR, Vol. 1, 2 (2022), 3.","journal-title":"ICLR"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"e_1_3_2_1_15_1","volume-title":"Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488","author":"Jiang Yihan","year":"2019","unstructured":"Yihan Jiang, Jakub Kone\u010dn\u1ef3, Keith Rush, and Sreeram Kannan. 2019. Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488 (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3225185"},{"key":"e_1_3_2_1_17_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_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00090"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"e_1_3_2_1_20_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_21_1","volume-title":"Newsweeder: Learning to filter netnews. In Machine learning proceedings","author":"Lang Ken","year":"1995","unstructured":"Ken Lang. 1995. Newsweeder: Learning to filter netnews. In Machine learning proceedings 1995. Elsevier, 331-339."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_2_1_23_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. 2021b. Ditto: Fair and robust federated learning through personalization. In International conference on machine learning. PMLR, 6357-6368."},{"key":"e_1_3_2_1_24_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"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, Vol. 2 (2020), 429-450.","journal-title":"Proceedings of Machine learning and systems"},{"key":"e_1_3_2_1_25_1","volume-title":"Fedbn: Federated learning on non-iid features via local batch normalization. arXiv preprint arXiv:2102.07623","author":"Li Xiaoxiao","year":"2021","unstructured":"Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. 2021c. Fedbn: Federated learning on non-iid features via local batch normalization. arXiv preprint arXiv:2102.07623 (2021)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00490"},{"key":"e_1_3_2_1_27_1","volume-title":"Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523","author":"Liang Paul Pu","year":"2020","unstructured":"Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3299851"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611966"},{"key":"e_1_3_2_1_30_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, Vol. 34 (2021), 5972-5984.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_31_1","volume-title":"Disentangled federated learning for tackling attributes skew via invariant aggregation and diversity transferring. arXiv preprint arXiv:2206.06818","author":"Luo Zhengquan","year":"2022","unstructured":"Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, and Tieniu Tan. 2022. Disentangled federated learning for tackling attributes skew via invariant aggregation and diversity transferring. arXiv preprint arXiv:2206.06818 (2022)."},{"key":"e_1_3_2_1_32_1","volume-title":"Nike-based fast privacy-preserving highdimensional data aggregation for mobile devices","author":"Mandal Kalikinkar","year":"2018","unstructured":"Kalikinkar Mandal, Guang Gong, and Chuyi Liu. 2018. Nike-based fast privacy-preserving highdimensional data aggregation for mobile devices. IEEE T Depend Secure (2018), 142-149."},{"key":"e_1_3_2_1_33_1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics","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 Artificial intelligence and statistics. PMLR, 1273-1282.","journal-title":"PMLR"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2015509117"},{"key":"e_1_3_2_1_35_1","volume-title":"Fully neural network based speech recognition on mobile and embedded devices. Advances in neural information processing systems","author":"Park Jinhwan","year":"2018","unstructured":"Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, and Wonyong Sung. 2018. Fully neural network based speech recognition on mobile and embedded devices. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"International Conference on Machine Learning. PMLR, 17716-17758","author":"Pillutla Krishna","year":"2022","unstructured":"Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, and Lin Xiao. 2022. Federated learning with partial model personalization. In International Conference on Machine Learning. PMLR, 17716-17758."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599344"},{"key":"e_1_3_2_1_38_1","volume-title":"Personalized federated learning with moreau envelopes. Advances in neural information processing systems","author":"Dinh Canh T","year":"2020","unstructured":"Canh T Dinh, Nguyen Tran, and Josh Nguyen. 2020. Personalized federated learning with moreau envelopes. Advances in neural information processing systems, Vol. 33 (2020), 21394-21405."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583347"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583264"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714884"},{"key":"e_1_3_2_1_43_1","volume-title":"Pelee: A real-time object detection system on mobile devices. Advances in neural information processing systems","author":"Wang Robert J","year":"2018","unstructured":"Robert J Wang, Xiang Li, and Charles X Ling. 2018. Pelee: A real-time object detection system on mobile devices. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3310536"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449926"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i20.35460"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681588"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01775"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449851"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01139"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3649363"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611781"},{"key":"e_1_3_2_1_53_1","volume-title":"pFedMoE: Data-level personalization with mixture of experts for model-heterogeneous personalized federated learning. arXiv preprint arXiv:2402.01350","author":"Yi Liping","year":"2024","unstructured":"Liping Yi, Han Yu, Chao Ren, Heng Zhang, Gang Wang, Xiaoguang Liu, and Xiaoxiao Li. 2024. pFedMoE: Data-level personalization with mixture of experts for model-heterogeneous personalized federated learning. arXiv preprint arXiv:2402.01350 (2024)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00465"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26330"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599345"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645544"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450051"},{"key":"e_1_3_2_1_59_1","volume-title":"Personalized Federated Learning with First Order Model Optimization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ehJqJQk9cw","author":"Zhang Michael","unstructured":"Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, and Jose M. Alvarez. 2021b. Personalized Federated Learning with First Order Model Optimization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ehJqJQk9cw"},{"key":"e_1_3_2_1_60_1","volume-title":"SPViT: Accelerate Vision Transformer Inference on Mobile Devices via Adaptive Splitting and Offloading","author":"Zhao Sifan","year":"2025","unstructured":"Sifan Zhao, Tongtong Liu, Hai Jin, and Dezhong Yao. 2025. SPViT: Accelerate Vision Transformer Inference on Mobile Devices via Adaptive Splitting and Offloading. IEEE Transactions on Mobile Computing (2025)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681260"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3316645"}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774904.3792140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:29:01Z","timestamp":1783150141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":62,"alternative-id":["10.1145\/3774904.3792140","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792140","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}