HCFSL-UAV: Hierarchical Cluster-Head Federated Split Learning for UAV Networks

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.