# Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks

arXiv: https://arxiv.org/abs/2412.00090 · HTML: https://mlsanity.com/paper/2412.00090/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [SpinQuant: LLM quantization with learned rotations](https://mlsanity.com/paper/2405.16406/) (2024)
- [OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://mlsanity.com/paper/2402.06954/) (2024)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices](https://mlsanity.com/paper/2012.11804/) (2020)
- [Gradient-based Parameter Selection for Efficient Fine-Tuning](https://mlsanity.com/paper/2312.10136/) (2023)
- [Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks](https://mlsanity.com/paper/2407.02924/) (2024)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning](https://mlsanity.com/paper/2301.00130/) (2022)
- [ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey](https://mlsanity.com/paper/2307.04251/) (2023)
- [Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Service](https://mlsanity.com/paper/2310.18602/) (2023)
- [Resource Allocation for Stable LLM Training in Mobile Edge Computing](https://mlsanity.com/paper/2409.20247/) (2024)
- [Resource-Efficient Personal Large Language Models Fine-Tuning with Collaborative Edge Computing](https://mlsanity.com/paper/2408.10746/) (2024)

## Built on by (most cited first, 3 of 3)

- [SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices](https://mlsanity.com/paper/2503.18986/) (2025)
- [AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning](https://mlsanity.com/paper/2607.17913/) (2026)
- [PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs](https://mlsanity.com/paper/2607.12111/) (2026)

## Abstract (arXiv)

In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynamics in edge networks, a underlineCut lunderlineAyer and computing underlineResource underlineDecision (CARD) algorithm is developed to minimize training delay and energy consumption. Simulation results demonstrate that the proposed approach reduces the average training delay and server's energy consumption by 70.8% and 53.1%, compared to the benchmarks, respectively.