发表机构
the University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对拆分联邦学习中混合整数优化难题，提出资源受限网络下的高效优化框架，联合优化模型拆分与资源分配，实现能耗-延迟最优权衡，可适配大规模用户场景。
AI 中文摘要
拆分联邦学习（SFL）已成为边缘侧模型训练的强大范式。然而，SFL本质上涉及模型拆分和资源分配的离散决策变量，会带来极具挑战性的混合整数问题。因此，现有的SFL优化方案要么是启发式的，要么计算效率低下，无法应对大规模用户群体。为解决这一局限，本研究针对资源受限网络构建了一套高效的SFL优化框架。该框架联合优化模型拆分与资源分配，以最小化训练成本——训练成本被定义为延迟与能耗成本的加权和。我们首先研究了模型拆分问题，提出了一种可实现全局最优的多项式时间算法。随后，我们将该方法拓展至模型拆分与资源分配的联合优化问题，将其建模为二维主问题，并提出了一种具备(1+ε)近似保证的高效近似方法。大量实验表明，所提方法可提供高效解决方案，实现能耗与延迟的最优权衡。
英文摘要
Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge. However, SFL inherently involves discrete decision variables for model splitting and resource allocation, resulting in a challenging mixed-integer problem. Consequently, prior optimization schemes for SFL are either \textit{heuristic} or \textit{computationally inefficient}, which cannot handle large-scale user populations. To address this limitation, this work establishes an efficient optimization framework for SFL under resource-constrained networks. Our framework jointly optimizes model splitting and resource allocation to minimize training cost, which is defined as the weighted sum of latency and energy costs. We first study the model splitting problem and develop a polynomial-time algorithm that achieves the global optimum. Then, we extend the approach to the joint model splitting and resource allocation problem. In this case, we formulate it as a two-dimensional master problem and develop an efficient approximation method with a $(1+ε)$-approximation guarantee. Extensive experiments show that the proposed approach provides efficient solutions to strike the optimal energy--latency tradeoff.
