# Privacy-Preserving Offloading for Large Language Models in 6G Vehicular Networks

arXiv: https://arxiv.org/abs/2509.05320 · HTML: https://mlsanity.com/paper/2509.05320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models](https://mlsanity.com/paper/2406.04845/) (2024)
- [Security and privacy for 6G: A survey on prospective technologies and challenges](https://mlsanity.com/paper/2108.11861/) (2021)
- [Resource Allocation in Large Language Model Integrated 6G Vehicular Networks](https://mlsanity.com/paper/2403.19016/) (2024)
- [A Survey of Transformer-based Language Models with Focus on Efficiency](https://mlsanity.com/paper/2406.16893/) (2024)
- [Large language models in 6G security: challenges and opportunities](https://mlsanity.com/paper/2403.12239/) (2024)
- [A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems](https://mlsanity.com/paper/2401.06831/) (2024)
- [Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning](https://mlsanity.com/paper/2308.02614/) (2023)

## Abstract (arXiv)

The integration of Large Language Models (LLMs) in 6G vehicular networks promises unprecedented advancements in intelligent transportation systems. However, offloading LLM computations from vehicles to edge infrastructure poses significant privacy risks, potentially exposing sensitive user data. This paper presents a novel privacy-preserving offloading framework for LLM-integrated vehicular networks. We introduce a hybrid approach combining federated learning (FL) and differential privacy (DP) techniques to protect user data while maintaining LLM performance. Our framework includes a privacy-aware task partitioning algorithm that optimizes the trade-off between local and edge computation, considering both privacy constraints and system efficiency. We also propose a secure communication protocol for transmitting model updates and aggregating results across the network. Experimental results demonstrate that our approach achieves 75% global accuracy with only a 2-3% reduction compared to non-privacy-preserving methods, while maintaining DP guarantees with an optimal privacy budget of varepsilon = 0.8. The framework shows stable communication overhead of approximately 2.1MB per round with computation comprising over 90% of total processing time, validating its efficiency for resource-constrained vehicular environments.