{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:25:12Z","timestamp":1780471512307,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Contactless palmprint recognition has recently emerged as a promising biometric technology. However, traditional methods that require sharing user data introduce substantial security risks. While federated learning offers privacy-preserving solutions, it often compromises recognition accuracy due to feature distribution drift caused by external factors such as lighting and devices. To address this issue, we propose an adaptive personalized federated learning framework (AdaptPFL). The central innovation lies in decomposing palmprint features into identity-related and contextual-related components using a feature decoupling mechanism. This design isolates the influence of external environmental factors on identity recognition through de-entanglement. Furthermore, two adaptive aggregation strategies are introduced to correct client drift: (1) Intra-Local Adaptive Aggregation (ILAA), which addresses intra-client drift by adaptively combining the two decoupled feature types; (2) Global-Local Adaptive Aggregation (GLAA), which corrects inter-client drift by adaptively aggregating model parameters. Experimental results demonstrate that AdaptPFL achieves superior performance compared to existing state-of-the-art methods.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/787","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"7074-7082","source":"Crossref","is-referenced-by-count":2,"title":["AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling"],"prefix":"10.24963","author":[{"given":"Zirui","family":"Zhang","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghai","family":"Guan","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c7etin Kaya","family":"Ko\u00e7","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wen","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology Shenzhen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:08Z","timestamp":1758627308000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/787"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/787","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}