# Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Service

arXiv: https://arxiv.org/abs/2310.18602 · HTML: https://mlsanity.com/paper/2310.18602/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2023.

## Built on by (most cited first, 7 of 7)

- [Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2404.06448/) (2024)
- [Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches](https://mlsanity.com/paper/2408.10691/) (2024)
- [Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge](https://mlsanity.com/paper/2407.09873/) (2024)
- [Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks](https://mlsanity.com/paper/2412.00090/) (2024)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization](https://mlsanity.com/paper/2411.06581/) (2024)
- [A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks](https://mlsanity.com/paper/2509.19306/) (2025)

## Abstract (arXiv)

Foundation models (FoMos), referring to large-scale AI models, possess human-like capabilities and are able to perform competitively in the domain of human intelligence. The breakthrough in FoMos has inspired researchers to deploy such models in the sixth-generation (6G) mobile networks for automating a broad range of tasks in next-generation mobile applications. While the sizes of FoMos are reaching their peaks, their next phase is expected to focus on fine-tuning the models to specific downstream tasks. This inspires us to propose the vision of FoMo fine-tuning as a 6G service. Its key feature is the exploitation of existing parameter-efficient fine-tuning (PEFT) techniques to tweak only a small fraction of model weights for a FoMo to become customized for a specific task. To materialize the said vision, we survey the state-of-the-art PEFT and then present a novel device-edge fine-tuning (DEFT) framework for providing efficient and privacy-preserving fine-tuning services at the 6G network edge. The framework consists of the following comprehensive set of techniques: 1) Control of fine-tuning parameter sizes in different transformer blocks of a FoMo; 2) Over-the-air computation for realizing neural connections in DEFT; 3) Federated DEFT in a multi-device system by downloading a FoMo emulator or gradients; 4) On-the-fly prompt-ensemble tuning; 5) Device-to-device prompt transfer among devices. Experiments are conducted using pre-trained FoMos with up to 11 billion parameters to demonstrate the effectiveness of DEFT techniques. The article is concluded by presenting future research opportunities.