mlsanityData to 7 Oct 2026

Personalized Wireless Federated Learning for Large Language Models

arXiv 2404.13238 ↗First posted 20 Apr 2024cs.LG cs.AI cs.CLcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

9 of 19 references.

References · 9

  1. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  2. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  3. 2020Efficient Content-Based Sparse Attention with Routing Transformerscited by 469
  4. 2021Towards Personalized Federated Learningcited by 462
  5. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
  6. 2022On the Effectiveness of Parameter-Efficient Fine-Tuningcited by 140
  7. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 62
  8. 2023Large Language Model Enhanced Multi-Agent Systems for 6G Communicationscited by 53
  9. 2023Low-Parameter Federated Learning with Large Language Modelscited by 22

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 1.

10 papers.

  1. 2024Federated LoRA with Sparse Communicationcited by 26
  2. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 25
  3. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  4. 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
  5. 2024Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architecturescited by 10
  6. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  7. 2025SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devicescited by 3
  8. 2025Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networkscited by 2
  9. 2026SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendationscited by 1
  10. 2025Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Frameworkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
  2. 2026Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
  3. 2026Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions
  4. 2025Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
  5. 2026FedPDPO: Federated Personalized Direct Preference Optimization for Large Language Model Alignment
  6. 2025Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
  7. 2024On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists
  8. 2026Concordia: Self-Improving Synthetic Tables for Federated LLMs