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

TL;DR

This paper introduces a Hierarchical Split Federated Learning framework for SAGIN, addressing resource constraints and data imbalance, with an optimization algorithm balancing training efficiency and accuracy.

Contribution

It proposes a novel HSFL framework with an upper bound analysis and an iterative optimization algorithm for device association and resource allocation in SAGIN.

Findings

Effective balance between training efficiency and model accuracy.

The proposed algorithm outperforms baseline methods in simulations.

Resource allocation improves FL performance in SAGIN environments.

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.