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

• URL: http://arxiv.org/abs/2601.13817v1
• Date: Tue, 20 Jan 2026 10:24:10 GMT
• Title: Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
• Authors: Haitao Zhao, Xiaoyu Tang, Bo Xu, Jinlong Sun, Linghao Zhang,
• Abstract summary: 6G facilitates deployment of Federated Learning (FL) in the Space-Air-Ground Integrated Network (SAGIN)<n>This paper proposes a Hierarchical Split Federated Learning framework and derives its upper bound of loss function.<n>We decompose the original problem into several subproblems, where an iterative optimization algorithm for device association and resource allocation is proposed.
• Score: 12.484890461669828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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