6G facilitates deployment of Federated Learning (FL) in the Space-Air-Ground Integrated Network (SAGIN), yet FL confronts challenges such as resource constrained and unbalanced data distribution. To address these issues, this paper proposes a Hierarchical Split Federated Learning (HSFL) framework and derives its upper bound of loss function. To minimize the weighted sum of training loss and latency, we formulate a joint optimization problem that integrates device association, model split layer selection, and resource allocation. We decompose the original problem into several subproblems, where an iterative optimization algorithm for device association and resource allocation based on brute-force split point search is proposed. Simulation results demonstrate that the proposed algorithm can effectively balance training efficiency and model accuracy for FL in SAGIN.

翻译：6G技术推动了联邦学习（FL）在天地一体化网络（SAGIN）中的部署，然而FL面临着资源受限和数据分布不均衡等挑战。为解决这些问题，本文提出了一种层次化分割联邦学习（HSFL）框架，并推导了其损失函数的上界。为了最小化训练损失与延迟的加权和，我们构建了一个联合优化问题，该问题整合了设备关联、模型分割层选择以及资源分配。我们将原问题分解为若干子问题，并提出了一种基于暴力分割点搜索的设备关联与资源分配迭代优化算法。仿真结果表明，所提算法能有效平衡SAGIN中联邦学习的训练效率与模型精度。