Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
This addresses resource and data distribution problems for Federated Learning in 6G space-air-ground networks, representing an incremental improvement with a domain-specific focus.
This paper tackles the challenges of resource constraints and unbalanced data distribution for Federated Learning in Space-Air-Ground Integrated Networks by proposing a Hierarchical Split Federated Learning framework and a joint optimization algorithm for device association and resource allocation, which simulation results show can effectively balance training efficiency and model accuracy.
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.