{"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":1758672902637,"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>Federated Learning (FL) enables collaborative model training across distributed devices without sharing raw data. Hierarchical Federated Learning (HFL) is a new paradigm of FL that leverages the Edge Servers (ESs) layer as an intermediary to perform partial local model aggregation in proximity, reducing core network transmission overhead. However, HFL faces new challenges: (1) The two-stage aggregation process between client-edge and edge-cloud results in a dual-layer non-IID issue, which may significantly compromise model training accuracy. (2) The heterogeneity and mobility of clients further impact model training efficiency. To address these challenges, we propose a novel Clustered Semi-Asynchronous Hierarchical Federated Learning (CSAHFL) framework that integrates adaptive semi-asynchronous intra-cluster aggregation at client-edge layer and dynamic distribution-aware inter-cluster aggregation at edge-cloud layer, collaboratively enhancing model performance and scalability in heterogeneous and mobile environments. We conducte experiments under varying degrees of dual-layer non-IID in both static and high-mobility scenarios. The results demonstrate significant advantages of CSAHFL over representative state-of-the-art methods.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/621","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"5580-5588","source":"Crossref","is-referenced-by-count":0,"title":["CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks"],"prefix":"10.24963","author":[{"given":"Aijing","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications, China"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, China"},{"name":"Century College, Beijing University of Posts and Telecommunications, 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, China"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, China"}]},{"given":"Dandan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications, China"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, China"},{"name":"Zaozhuang University, China"}]},{"given":"Yingxia","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications, China"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, China"}]},{"given":"Tong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications, China"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, China"}]},{"given":"Guanhua","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications, China"},{"name":"Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, 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":"2025-09-23T11:34:38Z","timestamp":1758627278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/621"}},"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\/621","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}