{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:02Z","timestamp":1758672902559,"version":"3.44.0"},"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>Personalized federated graph neural networks (PFGNN) are an emerging technology that allows multiple graph data owners to collaboratively train personalized models without sharing raw data. However, the Non-IID nature of graph data can cause the coupling of global and local knowledge parameters, which disrupts the optimization in personalized federated learning. Additionally, node neighbors may carry global and local knowledge, and their direct inclusion in training may introduce noise, degrading federated model performance. In this work, we propose the Adaptive Decoupling Personalized Federated Graph Neural Network (ADPFedGNN), which leverages multi-party collaboration to train personalized models for classifying local client graph nodes. We use two automatically updated masks and mutual information minimization to decouple global and local parameters in FGNN. We employ reinforcement learning to adaptively select appropriate neighbors for training global or local knowledge-related parameters while filtering out irrelevant nodes. We also design a personalized federated masked parameter aggregation mechanism that efficiently updates local personalized model parameters and aggregates the masked parameters.  Experimental results on three public datasets demonstrate that ADPFedGNN outperforms existing methods, achieving average improvements of 5.66 percent, 5.83 percent, and 12.45 percent in ACC, F1, and Recall, respectively.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/585","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"5253-5261","source":"Crossref","is-referenced-by-count":0,"title":["ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network"],"prefix":"10.24963","author":[{"given":"Zeli","family":"Guan","sequence":"first","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing 100876, PR China"}]},{"given":"Yawen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, PR China"}]},{"given":"Junping","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing 100876, PR China"}]},{"given":"Runqing","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing 100876, PR China"}]},{"given":"Xiaolong","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing 100876, PR China"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","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":"2025-09-23T11:34:29Z","timestamp":1758627269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/585"}},"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\/585","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}