{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:48:24Z","timestamp":1791953304816,"version":"4.3.4"},"reference-count":34,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Province Basic Research Special Project\u2011Key Project","award":["202601AS070111"],"award-info":[{"award-number":["202601AS070111"]}]},{"name":"Yunnan Provincial Department of Education Scientific Research Fund Project","award":["2025Y0670"],"award-info":[{"award-number":["2025Y0670"]}],"id":[{"id":"https:\/\/ror.org\/02yrxdp92","id-type":"ROR","asserted-by":"crossref"}]},{"award":["2025Y0670"],"award-info":[{"award-number":["2025Y0670"]}],"id":[{"id":"https:\/\/ror.org\/02yrxdp92","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Project of Educational Science Planning of Yunnan Province","award":["BC24012"],"award-info":[{"award-number":["BC24012"]}]},{"name":"Scientific Research Innovation Fund for Master Postgraduates of Yunnan Minzu University","award":["2026SKY058"],"award-info":[{"award-number":["2026SKY058"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Resource constraints, time-varying links, and heterogeneous data distributions make distributed model training difficult in unmanned aerial vehicle (UAV) networks. Conventional federated learning can impose substantial computational and communication burdens on resource-constrained UAVs, whereas existing split-learning schemes commonly rely on fixed split points and static participation strategies. To address these limitations, this paper proposes HCFSL-UAV, a hierarchical cluster-head federated split learning framework for UAV networks. The framework adopts a three-layer architecture composed of a central base station, cluster-head UAVs, and member UAVs, and integrates split learning within hierarchical federated aggregation. Member UAVs are dynamically associated with cluster heads according to their own real-time 3-D spatial locations. Within each cluster, participant selection and model splitting are jointly optimized as the set of selected UAVs affects data representativeness and synchronous training latency, while the split point determines the computation, memory, and communication requirements of participating UAVs. A joint optimization problem is formulated to balance data-distribution deviation, training latency, and resource consumption, and an alternating optimization procedure is developed using greedy participant selection and a discrete split-point search. Experiments on FMNIST, CIFAR-10, and CIFAR-100 show that HCFSL-UAV achieves faster convergence and higher test accuracy than the considered federated-learning and split-learning baselines, while reducing communication and computation overhead in the reported settings. These results demonstrate the effectiveness of jointly optimizing participant selection and adaptive model partitioning in dynamic and heterogeneous UAV networks.<\/jats:p>","DOI":"10.3390\/s26196331","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:43:38Z","timestamp":1791564218000},"page":"6331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HCFSL-UAV: Hierarchical Cluster-Head Federated Split Learning for UAV Networks"],"prefix":"10.3390","volume":"26","author":[{"given":"Ruomeng","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Yunnan Minzu University, Kunming 650504, China"},{"name":"University Key Laboratory of Information and Communication on Security Backup and Recovery, Yunnan Minzu University, Kunming 650504, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4458-2239","authenticated-orcid":false,"given":"Jing","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Yunnan Minzu University, Kunming 650504, China"},{"name":"University Key Laboratory of Information and Communication on Security Backup and Recovery, Yunnan Minzu University, Kunming 650504, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4491-1548","authenticated-orcid":false,"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Yunnan Minzu University, Kunming 650504, China"},{"name":"University Key Laboratory of Information and Communication on Security Backup and Recovery, Yunnan Minzu University, Kunming 650504, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Yunnan Minzu University, Kunming 650504, China"},{"name":"University Key Laboratory of Information and Communication on Security Backup and Recovery, Yunnan Minzu University, Kunming 650504, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12930","DOI":"10.1109\/ACCESS.2026.3656665","article-title":"More than a mobile client: A systematic literature review of architectures, applications, and challenges of UAV-enabled federated learning","volume":"14","author":"Najafinia","year":"2026","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53841","DOI":"10.1109\/ACCESS.2020.2981430","article-title":"Federated learning for UAVs-enabled wireless networks: Use cases, challenges, and open problems","volume":"8","author":"Brik","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119360","DOI":"10.1016\/j.ins.2023.119360","article-title":"FedRich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling","volume":"645","author":"Yang","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_4","unstructured":"Singh, A., Vepakomma, P., Gupta, O., and Raskar, R. (2019). Detailed comparison of communication efficiency of split learning and federated learning. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109380","DOI":"10.1016\/j.comnet.2022.109380","article-title":"ARES: Adaptive resource-aware split learning for Internet of Things","volume":"218","author":"Samikwa","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, Y., Hu, G., Teng, Y., and Cai, D. (2025, January 24\u201327). Split federated learning over heterogeneous edge devices: Algorithm and optimization. Proceedings of the 2025 IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy.","DOI":"10.1109\/WCNC61545.2025.10978241"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/TWC.2021.3103739","article-title":"UAV-enabled data collection for wireless sensor networks with distributed beamforming","volume":"21","author":"Feng","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7231","DOI":"10.1109\/TVT.2019.2915836","article-title":"Joint optimization of UAV position, time slot allocation, and computation task partition in multiuser aerial mobile-edge computing systems","volume":"68","author":"Hu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"136","DOI":"10.23919\/JCC.2022.01.011","article-title":"Joint power-trajectory-scheduling optimization in a mobile UAV-enabled network via alternating iteration","volume":"19","author":"Qi","year":"2022","journal-title":"China Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5505","DOI":"10.1109\/TII.2019.2948406","article-title":"Joint computation and communication design for UAV-assisted mobile edge computing in IoT","volume":"16","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1109\/JIOT.2019.2958975","article-title":"UAV-assisted wireless powered cooperative mobile edge computing: Joint offloading, CPU control, and trajectory optimization","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7894","DOI":"10.1109\/TWC.2023.3345839","article-title":"Generalized UAV deployment for UAV-assisted cellular networks","volume":"23","author":"Kim","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zeng, T., Semiari, O., Mozaffari, M., Chen, M., Saad, W., and Bennis, M. (2020, January 7\u201311). Federated learning in the sky: Joint power allocation and scheduling with UAV swarms. Proceedings of the IEEE International Conference on Communications (ICC), Dublin, Ireland.","DOI":"10.1109\/ICC40277.2020.9148776"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1109\/JIOT.2022.3152829","article-title":"Joint training and resource allocation optimization for federated learning in UAV swarm","volume":"10","author":"Shen","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1109\/TNSM.2022.3216326","article-title":"UAV-assisted online machine learning over multi-tiered networks: A hierarchical nested personalized federated learning approach","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1109\/TVT.2024.3390226","article-title":"An efficient 6G federated learning-enabled energy-efficient scheme for UAV deployment","volume":"74","author":"Raja","year":"2025","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3944","DOI":"10.1109\/TVT.2021.3065084","article-title":"UAV communications for sustainable federated learning","volume":"70","author":"Pham","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1109\/TVT.2022.3150004","article-title":"Energy-efficient federated learning over UAV-enabled wireless powered communications","volume":"71","author":"Pham","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1109\/TWC.2023.3303492","article-title":"Federated learning via unmanned aerial vehicle","volume":"23","author":"Fu","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1109\/TNSM.2025.3531237","article-title":"Energy-efficient UAV-assisted federated learning: Trajectory optimization, device scheduling, and resource management","volume":"22","author":"Fu","year":"2025","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2018.05.003","article-title":"Distributed learning of deep neural network over multiple agents","volume":"116","author":"Gupta","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_22","unstructured":"Vepakomma, P., Gupta, O., Swedish, T., and Raskar, R. (2018). Split learning for health: Distributed deep learning without sharing raw patient data. arXiv."},{"key":"ref_23","unstructured":"Thapa, C., Arachchige, P.C.M., Camtepe, S., and Sun, L. (March, January 22). SplitFed: When federated learning meets split learning. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event."},{"key":"ref_24","unstructured":"Hong, J., Wang, H., Wang, Z., and Zhou, J. (2022, January 25\u201329). Efficient split-mix federated learning for on-demand and in-situ customization. Proceedings of the International Conference on Learning Representations (ICLR), Virtual Event."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.1109\/TWC.2022.3213411","article-title":"Wireless distributed learning: A new hybrid split and federated learning approach","volume":"22","author":"Liu","year":"2023","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nishio, T., and Yonetani, R. (2019, January 20\u201324). Client selection for federated learning with heterogeneous resources in mobile edge. Proceedings of the IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref_27","doi-asserted-by":"crossref","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":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MWC.001.1900119","article-title":"Reliable federated learning for mobile networks","volume":"27","author":"Kang","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.1109\/TWC.2021.3052681","article-title":"Convergence of update aware device scheduling for federated learning at the wireless edge","volume":"20","author":"Amiri","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7690","DOI":"10.1109\/TWC.2020.3015671","article-title":"Scheduling for cellular federated edge learning with importance and channel awareness","volume":"19","author":"Ren","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_31","unstructured":"Cho, Y.J., Gupta, S., Joshi, G., and Ya\u011fan, O. (2020, January 1\u20134). Bandit-based communication-efficient client selection strategies for federated learning. Proceedings of the 2020 54th Asilomar Conference on Signals, Systems, and Computers, Virtual Conference, Pacific Grove, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yoshida, N., Nishio, T., Morikura, M., and Yamamoto, K. (2020, January 7\u201311). MAB-based client selection for federated learning with uncertain resources in mobile networks. Proceedings of the 2020 IEEE Globecom Workshops (GC Wkshps), Taipei, Taiwan.","DOI":"10.1109\/GCWkshps50303.2020.9367421"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7108","DOI":"10.1109\/TWC.2020.3008091","article-title":"Multi-armed bandit-based client scheduling for federated learning","volume":"19","author":"Xia","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5135","DOI":"10.1109\/TCOMM.2020.2992040","article-title":"UAV-to-ground communications: Channel modeling and UAV selection","volume":"68","author":"Bithas","year":"2020","journal-title":"IEEE Trans. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6331\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:15:52Z","timestamp":1791951352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,7]]},"references-count":34,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196331"],"URL":"https:\/\/doi.org\/10.3390\/s26196331","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,7]]}}}