{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T18:15:21Z","timestamp":1783188921688,"version":"3.54.6"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia Autonomous Region","doi-asserted-by":"publisher","award":["2024ZD26"],"award-info":[{"award-number":["2024ZD26"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004712","name":"Tianjin University of Science and Technology","doi-asserted-by":"publisher","award":["2023YFJM0007"],"award-info":[{"award-number":["2023YFJM0007"]}],"id":[{"id":"10.13039\/501100004712","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004712","name":"Tianjin University of Science and Technology","doi-asserted-by":"publisher","award":["2025YFSH0070"],"award-info":[{"award-number":["2025YFSH0070"]}],"id":[{"id":"10.13039\/501100004712","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004712","name":"Tianjin University of Science and Technology","doi-asserted-by":"publisher","award":["2026YFHH0043"],"award-info":[{"award-number":["2026YFHH0043"]}],"id":[{"id":"10.13039\/501100004712","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.114939","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T17:42:33Z","timestamp":1777225353000},"page":"114939","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Research on personalized federated learning algorithm based on Kolmogorov\u2013Smirnov test clustering"],"prefix":"10.1016","volume":"177","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114939_b1","volume":"vol. 4","author":"Bishop","year":"2006"},{"key":"10.1016\/j.engappai.2026.114939_b2","doi-asserted-by":"crossref","unstructured":"Cai, L., Chen, N., Cao, Y., He, J., Li, Y., 2023. FedCE: personalized federated learning method based on clustering ensembles. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 1625\u20131633.","DOI":"10.1145\/3581783.3612217"},{"key":"10.1016\/j.engappai.2026.114939_b3","series-title":"International Conference on Machine Learning","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021"},{"key":"10.1016\/j.engappai.2026.114939_b4","series-title":"2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking","first-page":"228","article-title":"Fedgroup: Efficient federated learning via decomposed similarity-based clustering","author":"Duan","year":"2021"},{"issue":"11","key":"10.1016\/j.engappai.2026.114939_b5","first-page":"2661","article-title":"Flexible clustered federated learning for client-level data distribution shift","volume":"33","author":"Duan","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.engappai.2026.114939_b6","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume":"33","author":"Ghosh","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114939_b7","article-title":"Adaptive client clustering for efficient federated learning over non-iid and imbalanced data","author":"Gong","year":"2022","journal-title":"IEEE Trans. Big Data"},{"issue":"1\u20132","key":"10.1016\/j.engappai.2026.114939_b8","first-page":"1","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"10.1016\/j.engappai.2026.114939_b9","series-title":"International Conference on Machine Learning","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","author":"Li","year":"2021"},{"key":"10.1016\/j.engappai.2026.114939_b10","series-title":"Fedbn: Federated learning on non-iid features via local batch normalization","author":"Li","year":"2021"},{"issue":"3","key":"10.1016\/j.engappai.2026.114939_b11","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.engappai.2026.114939_b12","doi-asserted-by":"crossref","unstructured":"Li, W., Zhu, Z., Huang, T., Sun, L., Yuan, C., 2022. Federated knowledge transfer for heterogeneous visual models. In: Proceedings of the 4th ACM International Conference on Multimedia in Asia. pp. 1\u20137.","DOI":"10.1145\/3551626.3564955"},{"issue":"1","key":"10.1016\/j.engappai.2026.114939_b13","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s11280-022-01046-x","article-title":"Multi-center federated learning: clients clustering for better personalization","volume":"26","author":"Long","year":"2023","journal-title":"World Wide Web"},{"key":"10.1016\/j.engappai.2026.114939_b14","article-title":"Personalized federated learning with adaptive batchnorm for healthcare","author":"Lu","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.engappai.2026.114939_b15","first-page":"15434","article-title":"Federated multi-task learning under a mixture of distributions","volume":"34","author":"Marfoq","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114939_b16","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.engappai.2026.114939_b17","series-title":"Fedbabu: Towards enhanced representation for federated image classification","author":"Oh","year":"2021"},{"key":"10.1016\/j.engappai.2026.114939_b18","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.neucom.2021.08.141","article-title":"Fedsim: Similarity guided model aggregation for federated learning","volume":"483","author":"Palihawadana","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114939_b19","first-page":"8124","article-title":"Fedsoft: Soft clustered federated learning with proximal local updating","volume":"vol. 36","author":"Ruan","year":"2022"},{"key":"10.1016\/j.engappai.2026.114939_b20","first-page":"8124","article-title":"Fedsoft: Soft clustered federated learning with proximal local updating","volume":"vol. 36","author":"Ruan","year":"2022"},{"key":"10.1016\/j.engappai.2026.114939_b21","article-title":"Federated multi-task learning","volume":"30","author":"Smith","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114939_b22","series-title":"Pfedsim: Similarity-aware model aggregation towards personalized federated learning","author":"Tan","year":"2023"},{"key":"10.1016\/j.engappai.2026.114939_b23","series-title":"Personalized federated learning with contextualized generalization","author":"Tang","year":"2021"},{"key":"10.1016\/j.engappai.2026.114939_b24","series-title":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"2296","article-title":"Exponentially consistent k-means clustering algorithm based on Kolmogrov-Smirnov test","author":"Wang","year":"2018"},{"issue":"3","key":"10.1016\/j.engappai.2026.114939_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3625558","article-title":"Heterogeneous federated learning: State-of-the-art and research challenges","volume":"56","author":"Ye","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.engappai.2026.114939_b26","first-page":"10092","article-title":"Parameterized knowledge transfer for personalized federated learning","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114939_b27","first-page":"11237","article-title":"Fedala: Adaptive local aggregation for personalized federated learning","volume":"vol. 37","author":"Zhang","year":"2023"},{"key":"10.1016\/j.engappai.2026.114939_b28","series-title":"Personalized federated learning with first order model optimization","author":"Zhang","year":"2020"},{"key":"10.1016\/j.engappai.2026.114939_b29","doi-asserted-by":"crossref","unstructured":"Zhuang, W., Wen, Y., Zhang, X., Gan, X., Yin, D., Zhou, D., Zhang, S., Yi, S., 2020. Performance optimization of federated person re-identification via benchmark analysis. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 955\u2013963.","DOI":"10.1145\/3394171.3413814"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626012212?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626012212?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:19:53Z","timestamp":1783185593000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626012212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":29,"alternative-id":["S0952197626012212"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114939","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Research on personalized federated learning algorithm based on Kolmogorov\u2013Smirnov test clustering","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114939","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114939"}}