# Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network

arXiv: https://arxiv.org/abs/2601.13817 · HTML: https://mlsanity.com/paper/2601.13817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [Accelerating Split Federated Learning over Wireless Communication Networks](https://mlsanity.com/paper/2310.15584/) (2023)
- [A Survey on Resource Management in Joint Communication and Computing-Embedded SAGIN](https://mlsanity.com/paper/2403.17400/) (2024)
- [Multi-Drone 3D Trajectory Planning and Scheduling in Drone Assisted Radio Access Networks](https://mlsanity.com/paper/1906.00777/) (2019)
- [Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2212.02075/) (2022)
- [Fair Resource Allocation For Hierarchical Federated Edge Learning in Space-Air-Ground Integrated Networks via Deep Reinforcement Learning with Hybrid Control](https://mlsanity.com/paper/2408.02501/) (2024)
- [ALANINE: A Novel Decentralized Personalized Federated Learning For Heterogeneous LEO Satellite Constellation](https://mlsanity.com/paper/2411.07752/) (2024)
- [Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover](https://mlsanity.com/paper/2408.09522/) (2024)

## Abstract (arXiv)

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.