Unmanned aerial vehicles (UAVs) with integrated sensing, computation, and communication (ISCC) capabilities have become key enablers of next-generation wireless networks. Federated edge learning (FEL) leverages UAVs as mobile learning agents to collect data, perform local model updates, and contribute to global model aggregation. However, existing UAV-assisted FEL systems face critical challenges, including excessive computational demands, privacy risks, and inefficient communication, primarily due to the requirement for full-model training on resource-constrained UAVs. To deal with aforementioned challenges, we propose Split Federated Learning for UAV-Enabled ISCC (SFLSCC), a novel framework that integrates split federated learning (SFL) into UAV-assisted FEL. SFLSCC optimally partitions model training between UAVs and edge servers, significantly reducing UAVs' computational burden while preserving data privacy. We conduct a theoretical analysis of UAV deployment, split point selection, data sensing volume, and client-side aggregation frequency, deriving closed-form upper bounds for the convergence gap. Based on these insights, we conceive a joint optimization problem to minimize the energy consumption required to achieve a target model accuracy. Given the non-convex nature of the problem, we develop a low-complexity algorithm to efficiently determine UAV deployment, split point selection, and communication frequency. Extensive simulations on a target motion recognition task validate the effectiveness of SFLSCC, demonstrating superior convergence performance and energy efficiency compared to baseline methods.

翻译：具备集成感知、计算与通信能力的无人机已成为下一代无线网络的关键使能技术。联邦边缘学习利用无人机作为移动学习代理，收集数据、执行本地模型更新并参与全局模型聚合。然而，现有无人机辅助的联邦边缘学习系统面临严峻挑战，包括过高的计算需求、隐私风险及低效通信，这主要源于资源受限的无人机需进行完整模型训练。为应对上述挑战，本文提出面向无人机集成感知、计算与通信的分割联邦学习框架，该创新框架将分割联邦学习集成至无人机辅助的联邦边缘学习中。该框架通过在无人机与边缘服务器间优化划分模型训练任务，显著降低无人机的计算负担，同时保障数据隐私。我们对无人机部署、分割点选择、数据感知量及客户端聚合频率进行了理论分析，推导出收敛间隙的闭式上界。基于这些理论见解，我们构建了联合优化问题以最小化达到目标模型精度所需的能耗。鉴于该问题的非凸特性，我们开发了一种低复杂度算法，可高效确定无人机部署策略、分割点选择方案及通信频率。在目标运动识别任务上的大量仿真验证了所提框架的有效性，其收敛性能与能量效率均优于基线方法。