{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:26:37Z","timestamp":1777728397011,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T00:00:00Z","timestamp":1755388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligenza Artificiale"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>\n                    With the rapid development of AI-driven personalized services, model training increasingly depends on highly sensitive user-side data, such as location, social behaviour, and biometric information. These data not only exhibit pronounced non-independent and identically distributed (non-IID) characteristics but also pose serious privacy risks when processed centrally. Achieving efficient personalized modelling while preserving data locality and privacy has thus become a critical challenge in the evolution of personalized AI. In recent years, personalized federated learning (PFL) has gained significant attention for its strong performance in addressing non-IID data challenges. However, existing approaches often fall short in effectively balancing collaborative efficiency with personalization. To overcome this limitation, we propose FedDAC, a dynamically adaptive, collaboration-enhanced personalized federated learning method. By quantitatively assessing the responsiveness of each parameter to non-IID data, FedDAC dynamically selects collaborative clients, ensuring effective cooperation while retaining personalized feature information. Extensive experiments on four benchmark datasets (EMNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet) under two pathological non-IID settings show that FedDAC consistently outperforms strong baselines. It improves accuracy by 1.5\u20133.2% on average, reaching 5.9% on highly heterogeneous tasks. The source code is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Lixinqin9\/FedDAC-MAIN\">https:\/\/github.com\/Lixinqin9\/FedDAC-MAIN<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/17248035251367166","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:44:52Z","timestamp":1767692692000},"page":"80-92","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Adaptive Collaboration for Personalized Federated Learning"],"prefix":"10.1177","volume":"20","author":[{"given":"LiXin","family":"Qin","sequence":"first","affiliation":[{"name":"Dalian Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5198-8377","authenticated-orcid":false,"given":"ChunLong","family":"Yao","sequence":"additional","affiliation":[{"name":"Dalian Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Dalian Cloud Power Technology Company, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,8,17]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"Arivazhagan M. 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Exploiting shared representations for personalized federated learning. In International conference on machine learning (pp. 2089\u20132099)."},{"key":"e_1_3_2_7_1","unstructured":"Deng Y. Kamani M. M. Mahdavi M. (2020). Adaptive personalized federated learning. arXiv preprint arXiv:2003.13461."},{"key":"e_1_3_2_8_1","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"Dinh C.","year":"2020","unstructured":"Dinh C., Tran N., Nguyen J. (2020). Personalized federated learning with moreau envelopes. Advances in Neural Information Processing Systems, 33, 21394\u201321405.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_9_1","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"33","author":"Fallah A.","year":"2020","unstructured":"Fallah A., Mokhtari A., Ozdaglar A. (2020). 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