# Personalized Wireless Federated Learning for Large Language Models

arXiv: https://arxiv.org/abs/2404.13238 · HTML: https://mlsanity.com/paper/2404.13238/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Efficient Content-Based Sparse Attention with Routing Transformers](https://mlsanity.com/paper/2003.05997/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Towards Building the Federated GPT: Federated Instruction Tuning](https://mlsanity.com/paper/2305.05644/) (2023)
- [On the Effectiveness of Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2211.15583/) (2022)
- [FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models](https://mlsanity.com/paper/2310.10049/) (2023)
- [Large Language Model Enhanced Multi-Agent Systems for 6G Communications](https://mlsanity.com/paper/2312.07850/) (2023)
- [Low-Parameter Federated Learning with Large Language Models](https://mlsanity.com/paper/2307.13896/) (2023)

## Built on by (most cited first, 11 of 11)

- [A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models](https://mlsanity.com/paper/2504.07070/) (2025)
- [Federated LoRA with Sparse Communication](https://mlsanity.com/paper/2406.05233/) (2024)
- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures](https://mlsanity.com/paper/2411.19128/) (2024)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning](https://mlsanity.com/paper/2406.04076/) (2024)
- [SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices](https://mlsanity.com/paper/2503.18986/) (2025)
- [SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations](https://mlsanity.com/paper/2601.10004/) (2026)
- [Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks](https://mlsanity.com/paper/2512.09485/) (2025)
- [Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework](https://mlsanity.com/paper/2501.09631/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges related to security and privacy. Federated Learning (FL), with its decentralized architecture, offers enhanced data privacy protection. Nevertheless, when integrated with LLMs, FL still struggles with several critical limitations, including large-scale and heterogeneous data, resource-intensive training, and substantial communication overhead. To address these challenges, this paper first presents a systematic analysis of the distinct training stages of LLMs in wireless networks, including pre-training, instruction tuning, and alignment tuning. Building upon this foundation, we propose a Personalized Wireless Federated Fine-tuning (PWFF) framework. Initially, we utilize the adapter and Low-Rank Adaptation (LoRA) techniques to decrease energy consumption, while employing global partial aggregation to reduce communication delay. Subsequently, we develop two reward models and design a personalized loss function to fulfill the goal of personalized learning. Furthermore, we implement a local multi-objective alignment to ensure the stability and effectiveness of the FL process. Finally, we conduct a series of simulations to validate the performance of the proposed PWFF method and provide an in-depth discussion of the open issues.