{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T07:04:17Z","timestamp":1766127857643,"version":"3.48.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation of Shanghai","award":["20ZR1421600"],"award-info":[{"award-number":["20ZR1421600"]}]},{"name":"Natural Science Foundation of Shanghai","award":["20ZR1421600"],"award-info":[{"award-number":["20ZR1421600"]}]},{"name":"Natural Science Foundation of Shanghai","award":["20ZR1421600"],"award-info":[{"award-number":["20ZR1421600"]}]},{"name":"Natural Science Foundation of Shanghai","award":["20ZR1421600"],"award-info":[{"award-number":["20ZR1421600"]}]},{"name":"Natural Science Foundation of Shanghai","award":["20ZR1421600"],"award-info":[{"award-number":["20ZR1421600"]}]},{"name":"Natural Science Foundation of Shanghai","award":["20ZR1421600"],"award-info":[{"award-number":["20ZR1421600"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining","award":["MIMS21-M-02"],"award-info":[{"award-number":["MIMS21-M-02"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining","award":["MIMS21-M-02"],"award-info":[{"award-number":["MIMS21-M-02"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining","award":["MIMS21-M-02"],"award-info":[{"award-number":["MIMS21-M-02"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining","award":["MIMS21-M-02"],"award-info":[{"award-number":["MIMS21-M-02"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining","award":["MIMS21-M-02"],"award-info":[{"award-number":["MIMS21-M-02"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining","award":["MIMS21-M-02"],"award-info":[{"award-number":["MIMS21-M-02"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s00530-025-02034-7","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T09:45:49Z","timestamp":1760694349000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized federated learning via multifaceted feature matching and element-wise classifier fusion"],"prefix":"10.1007","volume":"31","author":[{"given":"Yijun","family":"Cao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Botao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"issue":"6","key":"2034_CR1","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","volume":"2","author":"GA Kaissis","year":"2020","unstructured":"Kaissis, G.A., Makowski, M.R., R\u00fcckert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell 2(6), 305\u2013311 (2020). https:\/\/doi.org\/10.1038\/s42256-020-0186-1","journal-title":"Nat Mach Intell"},{"issue":"5","key":"2034_CR2","doi-asserted-by":"publisher","first-page":"8090","DOI":"10.1109\/JIOT.2023.3319986","volume":"11","author":"X Zhou","year":"2023","unstructured":"Zhou, X., Lei, X., Yang, C., Shi, Y., Zhang, X., Shi, J.: Handling data heterogeneity for iot devices in federated learning: a knowledge fusion approach. IEEE Internet Things J. 11(5), 8090\u20138104 (2023). https:\/\/doi.org\/10.1109\/JIOT.2023.3319986","journal-title":"IEEE Internet Things J."},{"key":"2034_CR3","doi-asserted-by":"crossref","unstructured":"Lu, R., Zhang, W., Wang, Y., Li, Q., Zhong, X., Yang, H., Wang, D.: (2023) Auction-based cluster federated learning in mobile edge computing systems. IEEE Transactions on Parallel and Distributed Systems 34(4), 1145\u20131158https:\/\/doi.org\/10.48550\/arXiv.2103.07150","DOI":"10.1109\/TPDS.2023.3240767"},{"key":"2034_CR4","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics 1273\u20131282 (2017). PMLR"},{"issue":"6","key":"2034_CR5","doi-asserted-by":"publisher","first-page":"4075","DOI":"10.1007\/s10586-022-03644-w","volume":"25","author":"Z Alamgir","year":"2022","unstructured":"Alamgir, Z., Khan, F.K., Karim, S.: Federated recommenders: methods, challenges and future. Clust. Comput. 25(6), 4075\u20134096 (2022). https:\/\/doi.org\/10.1007\/s10586-022-03644-w","journal-title":"Clust. Comput."},{"key":"2034_CR6","doi-asserted-by":"publisher","unstructured":"Kulkarni, V., Kulkarni, M., Pant, A.: Survey of personalization techniques for federated learning. In: 2020 fourth world conference on smart trends in systems, security and sustainability (WorldS4) 794\u2013797 (2020). https:\/\/doi.org\/10.1109\/worlds450073.2020.9210355 . IEEE","DOI":"10.1109\/worlds450073.2020.9210355"},{"issue":"12","key":"2034_CR7","doi-asserted-by":"publisher","first-page":"9587","DOI":"10.1109\/TNNLS.2022.3160699","volume":"34","author":"AZ Tan","year":"2022","unstructured":"Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. IEEE Trans Neural Networks Learning Syst 34(12), 9587\u20139603 (2022)","journal-title":"IEEE Trans Neural Networks Learning Syst"},{"issue":"8","key":"2034_CR8","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/tpami.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013). https:\/\/doi.org\/10.1109\/tpami.2013.50","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2034_CR9","unstructured":"Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated learning with personalization layers. arXiv preprint (2019). arXiv:1912.00818"},{"key":"2034_CR10","unstructured":"Liang, P.P., Liu, T., Ziyin, L., Allen, N.B., Auerbach, R.P., Brent, D., Salakhutdinov, R., Morency, L.-P.: Think locally, act globally: Federated learning with local and global representations. arXiv preprint (2020). arXiv:2001.01523"},{"key":"2034_CR11","unstructured":"Collins, L., Hassani, H., Mokhtari, A., Shakkottai, S.: Exploiting shared representations for personalized federated learning. In: International conference on machine learning 2089\u20132099 (2021). PMLR"},{"key":"2034_CR12","unstructured":"Xu, J., Tong, X., Huang, S.-L.: Personalized federated learning with feature alignment and classifier collaboration. arXiv preprint (2023). arXiv:2306.11867"},{"issue":"6","key":"2034_CR13","doi-asserted-by":"publisher","first-page":"6731","DOI":"10.1109\/TMC.2023.3325366","volume":"23","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Zhang, J., Tsang, D.H.: Fedfa: federated learning with feature anchors to align features and classifiers for heterogeneous data. IEEE Trans. Mob. Comput. 23(6), 6731\u20136742 (2023). https:\/\/doi.org\/10.1109\/TMC.2023.3325366","journal-title":"IEEE Trans. Mob. Comput."},{"key":"2034_CR14","doi-asserted-by":"publisher","unstructured":"Wang, L., Xu, S., Wang, X., Zhu, Q.: Addressing class imbalance in federated learning. In: Proceedings of the AAAI conference on artificial intelligence 35, 10165\u201310173 (2021). https:\/\/doi.org\/10.5121\/csit.2023.130522","DOI":"10.5121\/csit.2023.130522"},{"key":"2034_CR15","doi-asserted-by":"publisher","unstructured":"Fang, X., Ye, M.: Robust federated learning with noisy and heterogeneous clients. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 10072\u201310081 (2022). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00983","DOI":"10.1109\/cvpr52688.2022.00983"},{"key":"2034_CR16","unstructured":"Yoon, T., Shin, S., Hwang, S.J., Yang, E.: Fedmix: Approximation of mixup under mean augmented federated learning. arXiv preprint (2021). arXiv:2107.00233"},{"key":"2034_CR17","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proceedings of machine learning and systems 2, 429\u2013450 (2020)"},{"key":"2034_CR18","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: Stochastic controlled averaging for federated learning. In: International conference on machine learning 5132\u20135143 (2020). PMLR"},{"key":"2034_CR19","doi-asserted-by":"publisher","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 10713\u201310722 (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01057","DOI":"10.1109\/cvpr46437.2021.01057"},{"issue":"5","key":"2034_CR20","doi-asserted-by":"publisher","first-page":"3099","DOI":"10.1109\/tnse.2022.3146399","volume":"9","author":"H Wu","year":"2022","unstructured":"Wu, H., Wang, P.: Node selection toward faster convergence for federated learning on non-iid data. IEEE Trans Netw Sci Eng 9(5), 3099\u20133111 (2022). https:\/\/doi.org\/10.1109\/tnse.2022.3146399","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"2034_CR21","unstructured":"Fraboni, Y., Vidal, R., Kameni, L., Lorenzi, M.: Clustered sampling: Low-variance and improved representativity for clients selection in federated learning. In: International conference on machine learning 3407\u20133416 (2021). PMLR"},{"key":"2034_CR22","unstructured":"Zhu, Z., Hong, J., Zhou, J.: Data-free knowledge distillation for heterogeneous federated learning. In: International conference on machine learning 12878\u201312889 (2021). PMLR"},{"key":"2034_CR23","doi-asserted-by":"publisher","unstructured":"Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J., Zhang, C.: Fedproto: Federated prototype learning across heterogeneous clients. In: Proceedings of the AAAI conference on artificial intelligence 36, 8432\u20138440 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i8.20819","DOI":"10.1609\/aaai.v36i8.20819"},{"issue":"5","key":"2034_CR24","doi-asserted-by":"publisher","first-page":"8604","DOI":"10.1109\/jiot.2023.3320250","volume":"11","author":"Y Qiao","year":"2023","unstructured":"Qiao, Y., Munir, M.S., Adhikary, A., Le, H.Q., Raha, A.D., Zhang, C., Hong, C.S.: Mp-fedcl: multiprototype federated contrastive learning for edge intelligence. IEEE Internet Things J. 11(5), 8604\u20138623 (2023). https:\/\/doi.org\/10.1109\/jiot.2023.3320250","journal-title":"IEEE Internet Things J."},{"key":"2034_CR25","doi-asserted-by":"publisher","first-page":"4224","DOI":"10.1109\/tsp.2023.3314277","volume":"71","author":"R Ye","year":"2023","unstructured":"Ye, R., Ni, Z., Xu, C., Wang, J., Chen, S., Eldar, Y.C.: Fedfm: Anchor-based feature matching for data heterogeneity in federated learning. IEEE Trans Signal Process 71, 4224\u20134239 (2023). https:\/\/doi.org\/10.1109\/tsp.2023.3314277","journal-title":"IEEE Trans Signal Process"},{"key":"2034_CR26","unstructured":"Smith, V., Chiang, C.-K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. Advances in neural information processing systems 30 (2017)"},{"key":"2034_CR27","first-page":"21394","volume":"33","author":"CT Dinh","year":"2020","unstructured":"Dinh, C.T., Tran, N., Nguyen, J.: Personalized federated learning with moreauenvelopes. Adv Neural Inform Processing Syst 33, 21394\u201321405 (2020)","journal-title":"Adv Neural Inform Processing Syst"},{"key":"2034_CR28","unstructured":"Li, T., Hu, S., Beirami, A., Smith, V.: Ditto: Fair and robust federated learning through personalization. In: International conference on machine learning 6357\u20136368 (2021). PMLR"},{"key":"2034_CR29","unstructured":"Hanzely, F., Richt\u00e1rik, P.: Federated learning of a mixture of global and local models. arXiv preprint (2020). arXiv:2002.05516"},{"key":"2034_CR30","unstructured":"Deng, Y., Kamani, M.M., Mahdavi, M.: Adaptive personalized federated learning. arXiv preprint (2020). arXiv:2003.13461"},{"key":"2034_CR31","unstructured":"Deng, Y., Kamani, M.M., Mahdavi, M.: Adaptive personalized federated learning. arXiv preprint (2020). arXiv:2003.13461"},{"key":"2034_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., Guan, H.: Fedala: Adaptive local aggregation for personalized federated learning. In: Proceedings of the AAAI conference on artificial intelligence 37, 11237\u201311244 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i9.26330","DOI":"10.1609\/aaai.v37i9.26330"},{"issue":"8","key":"2034_CR33","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., Samek, W.: Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans Neural Netw Learning Syst 32(8), 3710\u20133722 (2020). https:\/\/doi.org\/10.1109\/tnnls.2020.3015958","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"key":"2034_CR34","doi-asserted-by":"crossref","unstructured":"Duan, M., Liu, D., Ji, X., Liu, R., Liang, L., Chen, X., Tan, Y.: Fedgroup: Efficient clustered federated learning via decomposed data-driven measure. arXiv preprint (2020). arXiv:2010.06870","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00042"},{"key":"2034_CR35","doi-asserted-by":"publisher","unstructured":"Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., Zhang, Y.: Personalized cross-silo federated learning on non-iid data. In: Proceedings of the AAAI conference on artificial intelligence 35, 7865\u20137873 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i9.16960","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"2034_CR36","unstructured":"Zhang, M., Sapra, K., Fidler, S., Yeung, S., Alvarez, J.M.: Personalized federated learning with first order model optimization. arXiv preprint (2020). arXiv:2012.08565"},{"key":"2034_CR37","doi-asserted-by":"publisher","unstructured":"Luo, J., Wu, S.: Adapt to adaptation: Learning personalization for cross-silo federated learning. In: IJCAI: proceedings of the conference 2022, 2166 (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/301","DOI":"10.24963\/ijcai.2022\/301"},{"key":"2034_CR38","unstructured":"Chen, H.-Y., Chao, W.-L.: On bridging generic and personalized federated learning for image classification. arXiv preprint (2021). arXiv:2107.00778"},{"key":"2034_CR39","doi-asserted-by":"publisher","unstructured":"Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., Cao, J., Guan, H.: Gpfl: Simultaneously learning global and personalized feature information for personalized federated learning. In: Proceedings of the IEEE\/CVF international conference on computer vision 5041\u20135051 (2023). https:\/\/doi.org\/10.1109\/iccv51070.2023.00465","DOI":"10.1109\/iccv51070.2023.00465"},{"key":"2034_CR40","doi-asserted-by":"publisher","first-page":"113073","DOI":"10.1016\/j.knosys.2025.113073","volume":"311","author":"S Zheng","year":"2025","unstructured":"Zheng, S., Zhu, Q., Lin, Q., Liu, S., Wong, K.-C., Li, J.: Personalized federated learning with multiple classifier aggregation. Knowledge-Based Syst 311, 113073 (2025). https:\/\/doi.org\/10.1016\/j.knosys.2025.113073","journal-title":"Knowledge-Based Syst"},{"key":"2034_CR41","doi-asserted-by":"publisher","first-page":"77038","DOI":"10.52202\/079017-2451","volume":"37","author":"C Mclaughlin","year":"2024","unstructured":"Mclaughlin, C., Su, L.: Personalized federated learning via feature distribution adaptation. Adv. Neural. Inf. Process. Syst. 37, 77038\u201377059 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2034_CR42","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning 1597\u20131607 (2020). PmLR"},{"key":"2034_CR43","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint (2017). arXiv:1708.07747"},{"key":"2034_CR44","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. (2009)"},{"key":"2034_CR45","unstructured":"Darlow, L.N., Crowley, E.J., Antoniou, A., Storkey, A.J.: Cinic-10 is not imagenet or cifar-10. arXiv preprint (2018). arXiv:1810.03505"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02034-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-02034-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02034-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T06:59:54Z","timestamp":1766127594000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-02034-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":45,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2034"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-02034-7","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"7 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","order":3,"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"}}],"article-number":"453"}}