{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:54:12Z","timestamp":1770674052384,"version":"3.49.0"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Commun. Netw."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Federated learning (FL) enables model training on edge devices using local data while aggregating model updates at a central server without exchanging raw data, thereby preserving privacy. However, achieving satisfactory convergence accuracy with low communication energy remains challenging. This work investigates a three-tier clustered FL (CFL) architecture to improve global training performance and communication efficiency through joint device clustering and resource scheduling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyze how clustering strategies influence learning convergence and communication energy consumption. Based on this analysis, we propose a clustering method that jointly accounts for gradient cosine similarity and communication distance. A simplified procedure is further developed for device association and cluster-head selection, with the goals of improving intra-cluster data balance and reducing the overall communication distance to the server.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Simulations demonstrate that the proposed method consistently improves model accuracy while reducing communication energy consumption compared with random clustering and similarity-based clustering baselines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These results indicate that jointly considering update similarity and communication distance in CFL can effectively balance learning quality and communication cost, offering a practical approach for energy-efficient federated training in edge networks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frcmn.2026.1748815","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T07:29:16Z","timestamp":1770622156000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Data- and distance-aware clustering for scalable wireless federated learning"],"prefix":"10.3389","volume":"7","author":[{"given":"Zhenning","family":"Chen","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihe","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Wireless Communications and IoT, Nanjing University of Posts and Telecommunications","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University","place":["Dalian, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youren","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/IJCNN48605.2020.9207469","article-title":"Federated learning with hierarchical clustering of local updates to improve training on non-iid data","volume-title":"2020 international joint conference on neural networks (IJCNN)","author":"Briggs","year":"2020"},{"key":"B2","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/twc.2020.3024629","article-title":"A joint learning and communications framework for federated learning over wireless networks","volume":"20","author":"Chen","year":"2020","journal-title":"IEEE Transactions Wireless Communications"},{"key":"B3","first-page":"80","article-title":"Clustered federated learning framework with acceleration based on data similarity","volume-title":"International conference on algorithms and architectures for parallel processing","author":"Gao","year":"2023"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2006.04088","article-title":"An efficient framework for clustered federated learning","author":"Ghosh","year":"2021"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1511.03575","article-title":"Federated optimization: distributed optimization beyond the datacenter","author":"Kone\u010dn\u1ef3","year":"2015","journal-title":"arXiv Preprint arXiv:1511.03575"},{"key":"B6","doi-asserted-by":"publisher","first-page":"19188","DOI":"10.1109\/JIOT.2024.3376548","article-title":"Federated learning with non-iid data: a survey","volume":"11","author":"Lu","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"B7","doi-asserted-by":"publisher","first-page":"9226","DOI":"10.3390\/s23229226","article-title":"An optimization method for non-iid federated learning based on deep reinforcement learning","volume":"23","author":"Meng","year":"2023","journal-title":"Sensors"},{"key":"B8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ICC.2019.8761315","article-title":"Client selection for federated learning with heterogeneous resources in Mobile edge","volume-title":"ICC 2019 - 2019 IEEE International Conference on Communications (ICC)","author":"Nishio","year":"2019"},{"key":"B9","doi-asserted-by":"publisher","first-page":"25371","DOI":"10.1109\/TITS.2022.3149860","article-title":"Clustered vehicular federated learning: process and optimization","volume":"23","author":"Ta\u00efk","year":"2022","journal-title":"IEEE Trans. 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