mlsanityData to 14 Oct 2026

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

arXiv 2412.00090 ↗First posted 27 Nov 2024cs.LG cs.CL cs.DCcited by 3

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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.

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

12 of 16 references.

References · 12

  1. 2024SpinQuant: LLM quantization with learned rotationscited by 471
  2. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  3. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  4. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
  5. 2023Gradient-based Parameter Selection for Efficient Fine-Tuningcited by 40
  6. 2024Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networkscited by 25
  7. 2024Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6Gcited by 15
  8. 2022Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learningcited by 10
  9. 2023ChatGPT in the Age of Generative AI and Large Language Models: A Concise Surveycited by 10
  10. 2023Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Servicecited by 7
  11. 2024Resource Allocation for Stable LLM Training in Mobile Edge Computingcited by 7
  12. 2024Resource-Efficient Personal Large Language Models Fine-Tuning with Collaborative Edge Computingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devicescited by 3
  2. 2026AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuningcited by 1
  3. 2026PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMscited by 0