6G环境下优化联邦学习的设备关联与资源分配
Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
- 分层拆分联邦学习框架，合理分配模型计算任务
- 算法显著降低训练损失与延迟，提升效率和精度平衡
- 适合研究6G智能边缘计算与分布式学习的读者
6G推动联邦学习在空-天-地一体化网络（SAGIN）中的部署，但面临资源受限和数据分布不均的挑战。为此，本文提出分层拆分联邦学习（HSFL）框架，并推导其损失函数上界。为最小化训练损失与延迟的加权和，构建了融合设备关联、模型拆分层选择与资源分配的联合优化问题。通过分解为多个子问题，提出基于暴力搜索拆分点的迭代优化算法。仿真结果表明，该算法能有效平衡SAGIN中联邦学习的训练效率与模型精度。
原文摘要 · Abstract (English)
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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