提出高效优化框架，解决边缘计算中模型分割与资源分配的难题。
Efficient Resource Optimization for Split Federated Learning
- 设计多项式时间算法求解模型分割最优解。
- 实现能量-延迟权衡的近似最优，保证(1+ε)逼近率。
- 适用于大规模用户场景，兼顾效率与精度。
分割联邦学习（SFL）已成为边缘模型训练的重要范式。然而，SFL天然涉及模型分割与资源分配的离散决策变量，导致混合整数优化难题。此前方法要么为启发式，要么计算效率低，难以应对大规模用户。本文提出一种面向资源受限网络的高效优化框架，联合优化模型分割与资源分配，以最小化训练成本——即延迟与能耗的加权和。首先研究模型分割问题，提出多项式时间算法获得全局最优解；随后拓展至联合优化问题，将其建模为二维主问题，并设计具有(1+ε)逼近保证的高效近似方法。大量实验表明，该方法能有效实现能量-延迟权衡的最优解。
原文摘要 · Abstract (English)
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.
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