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在天地一体化网络中的部署，然而联邦学习仍面临资源受限与数据分布不均衡等挑战。为解决这些问题，本文提出一种分层拆分联邦学习框架，并推导了其损失函数的上界。为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、模型拆分点选择与资源分配。我们将原问题分解为若干子问题，提出了一种基于暴力拆分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天地一体化网络中联邦学习的训练效率与模型精度。