# Efficient Resource Optimization for Split Federated Learning

arXiv: https://arxiv.org/abs/2608.17849 · HTML: https://mlsanity.com/paper/2608.17849/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing](https://mlsanity.com/paper/2007.07122/) (2020)
- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [Reducing Energy Bloat in Large Model Training](https://mlsanity.com/paper/2312.06902/) (2023)
- [SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving](https://mlsanity.com/paper/2408.05235/) (2024)
- [Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling](https://mlsanity.com/paper/2501.08219/) (2025)
- [ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices](https://mlsanity.com/paper/2402.15903/) (2024)
- [Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference](https://mlsanity.com/paper/2305.05933/) (2023)
- [Optimizing Split Federated Learning with Unstable Client Participation](https://mlsanity.com/paper/2509.17398/) (2025)
- [Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay](https://mlsanity.com/paper/2202.00329/) (2022)
- [Pipelining Split Learning in Multi-hop Edge Networks](https://mlsanity.com/paper/2505.04368/) (2025)
- [SLIDE: Simultaneous Model Downloading and Inference at the Wireless Network Edge](https://mlsanity.com/paper/2512.20946/) (2025)
- [SiftMoE: Similarity-Aware Energy-Efficient Expert Selection for Wireless Distributed MoE Inference](https://mlsanity.com/paper/2603.23888/) (2026)
- [ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning](https://mlsanity.com/paper/2603.04437/) (2026)

## Abstract (arXiv)

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 textitheuristic or textitcomputationally 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.