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Appl."],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>Federated learning-based person re-identification (Re-ID) aims to address the issue of data silos in surveillance systems caused by increasingly stringent regulations on sensitive data. However, due to differences in data collection locations, times, and scales, severe non-independent and identically distributed (non-IID) characteristics exist across different Re-ID datasets. Existing federated learning-based Re-ID methods often adopt a unified model structure, which prevents the model from adapting well to diverse data environments, thereby significantly degrading the overall Re-ID performance. To address the challenges of training neural networks on non-IID data across different datasets, we propose a customizable federated learning framework. First, customizable clients allow each organization to freely select suitable neural network training methods and model architectures based on local data scales and prior knowledge, thus improving training outcomes. Second, since traditional federated learning frameworks cannot achieve knowledge fusion through parameter exchange between models with different architectures, we introduce an independent model, referred to as the interaction model, specifically designed for knowledge exchange among clients. The interaction model learns parameters (knowledge) from local models on each client through distillation learning. Subsequently, the interaction model is uploaded to the server, where it undergoes parameter fusion (knowledge exchange) with interaction models from other clients. Finally, the interaction model, enriched with knowledge from other clients, guides local model training through knowledge distillation. It is worth noting that selecting a lightweight interaction model, while potentially impacting Re-ID performance, can significantly reduce communication costs between the server and clients.<\/jats:p>","DOI":"10.1145\/3735134","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T11:37:36Z","timestamp":1749555456000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["CCFL: Customized Client Federated Learning for Unsupervised Person Re-identification"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-4600","authenticated-orcid":false,"given":"Yi","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Information and Electronics Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6207-0299","authenticated-orcid":false,"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6649-7660","authenticated-orcid":false,"given":"Fayao","family":"Liu","sequence":"additional","affiliation":[{"name":"Agency for Science, Technology and Research, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3564-5090","authenticated-orcid":false,"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5418-9879","authenticated-orcid":false,"given":"Hancheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9234-0912","authenticated-orcid":false,"given":"Wenliang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"issue":"4","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501813","article-title":"Federated learning for healthcare: Systematic review and architecture proposal","volume":"13","author":"Stoffel Antunes Rodolfo","year":"2022","unstructured":"Rodolfo Stoffel Antunes, Cristiano Andr\u00e9 da Costa, Arne K\u00fcderle, Imrana Abdullahi Yari, and Bj\u00f6rn Eskofier. 2022. 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