{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T15:53:32Z","timestamp":1769270012979,"version":"3.49.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Anhui Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["Grant No. 2008085J31"],"award-info":[{"award-number":["Grant No. 2008085J31"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No.62276245"],"award-info":[{"award-number":["Grant No.62276245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10994-023-06443-5","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T12:37:29Z","timestamp":1705495049000},"page":"3869-3888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Communication-efficient clustered federated learning via model distance"],"prefix":"10.1007","volume":"113","author":[{"given":"Mao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Tie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yifei","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Changcun","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Haoyu","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Deqiang","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0227-3793","authenticated-orcid":false,"given":"Linli","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"issue":"1","key":"6443_CR1","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/TNNLS.2013.2256797","volume":"25","author":"H Chen","year":"2013","unstructured":"Chen, H., Tino, P., Rodan, A., et al. (2013). Learning in the model space for cognitive fault diagnosis. IEEE Transactions on Neural Networks and Learning Systems, 25(1), 124\u2013136.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6443_CR2","doi-asserted-by":"crossref","unstructured":"Fu, Y., Liu, X., & Tang, S., et.al. (2021) Cic-fl: Enabling class imbalance-aware clustered federated learning over shifted distributions. In International conference on database systems for advanced applications (pp. 37\u201352). Springer.","DOI":"10.1007\/978-3-030-73194-6_3"},{"key":"6443_CR3","unstructured":"Ghosh, A., Hong, J., & Yin, D. (2019). Robust federated learning in a heterogeneous environment. arXiv preprint arXiv:1906.06629"},{"key":"6443_CR4","unstructured":"Ghosh, A., Chung, J., & Yin, D., et\u00a0al. (2020). An efficient framework for clustered federated learning. arXiv preprint arXiv:2006.04088"},{"key":"6443_CR5","doi-asserted-by":"crossref","unstructured":"Huang, Y., Chu, L., Zhou, Z., et\u00a0al. (2021). Personalized cross-silo federated learning on non-iid data. In Proceedings of the AAAI conference on artificial intelligence (pp. 7865\u20137873).","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"6443_CR6","unstructured":"Kairouz, P., McMahan, H. B., Avent, B., et al. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977"},{"key":"6443_CR7","unstructured":"Karimireddy, S. P., Kale, S., & Mohri, M. (2020). Scaffold: Stochastic controlled averaging for federated learning. In ICML, PMLR (pp. 5132\u20135143)."},{"key":"6443_CR8","unstructured":"Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"},{"issue":"3","key":"6443_CR9","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., & Sahu, A. K. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50\u201360.","journal-title":"IEEE Signal Processing Magazine"},{"key":"6443_CR10","unstructured":"Li, X., Huang, K., & Yang, W., et.al. (2019). On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189"},{"key":"6443_CR11","first-page":"2351","volume":"33","author":"T Lin","year":"2020","unstructured":"Lin, T., Kong, L., Stich, S. U., et al. (2020). Ensemble distillation for robust model fusion in federated learning. NIPS, 33, 2351\u20132363.","journal-title":"NIPS"},{"key":"6443_CR12","doi-asserted-by":"crossref","unstructured":"Liu, B., Guo, Y., & Chen, X. (2021). Pfa: Privacy-preserving federated adaptation for effective model personalization. In Proceedings of the web conference (vol. 2021, pp. 923\u2013934).","DOI":"10.1145\/3442381.3449847"},{"key":"6443_CR13","unstructured":"Mavi, A. (2020) A new dataset and proposed convolutional neural network architecture for classification of american sign language digits. arXiv preprint arXiv:2011.08927"},{"key":"6443_CR14","unstructured":"McMahan, B., Moore, E., & Ramage, D. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, PMLR (pp. 1273\u20131282)."},{"issue":"8","key":"6443_CR15","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","volume":"32","author":"F Sattler","year":"2020","unstructured":"Sattler, F., & M\u00fcller, K. R. (2020). Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints. IEEE Transactions on Neural Networks and Learning Systems, 32(8), 3710\u20133722.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6443_CR16","unstructured":"Smith, V., Chiang, C. K., Sanjabi, M., et al. (2017). Federated multi-task learning. arXiv preprint arXiv:1705.10467"},{"key":"6443_CR17","unstructured":"Wang, H., Yurochkin, M. (2020). Federated learning with matched averaging. arXiv preprint arXiv:2002.06440"},{"key":"6443_CR18","unstructured":"Wang, K., Mathews, R., Kiddon, C., et\u00a0al. (2019). Federated evaluation of on-device personalization. arXiv preprint arXiv:1910.10252"},{"key":"6443_CR19","unstructured":"Zhang, M., Sapra, K., Fidler, S., et.al. (2020). Personalized federated learning with first order model optimization. arXiv preprint arXiv:2012.08565"},{"key":"6443_CR20","unstructured":"Zhu, Z., Hong, J., Zhou, J. (2021). Data-free knowledge distillation for heterogeneous federated learning. In ICML, PMLR (pp. 12878\u201312889)."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06443-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06443-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06443-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T18:09:03Z","timestamp":1764266943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06443-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,17]]},"references-count":20,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["6443"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06443-5","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,17]]},"assertion":[{"value":"6 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors agree to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors agree to the publication of the data and images in this paper.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}