Efficient Resource Optimization for Split Federated Learning

2026-08-18

AI Summary

本文针对分裂联邦学习中的资源优化问题，提出了一种有效框架，通过联合优化模型分割和资源分配来最小化训练成本。

Abstract

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.

Problem

• Split Federated Learning
• Resource Optimization
• Model Splitting
• Resource Allocation
• Mixed-Integer Problem

Innovation

• Efficient Optimization Framework
• Split Federated Learning
• Resource Allocation
• Polynomial-time Algorithm
• (1+ε)-approximation