6G车载网络中保护大模型计算隐私的高效卸载方案
Privacy-Preserving Offloading for Large Language Models in 6G Vehicular Networks
- 融合联邦学习与差分隐私，动态划分任务以平衡隐私与效率
- 实现75%全局准确率，隐私预算ε=0.8下仅损失2-3%
- 适合资源受限的车载场景，通信开销稳定在每轮2.1MB
6G车联网集成大语言模型（LLMs）有望推动智能交通系统跨越式发展。然而，将车辆端的模型计算卸载至边缘基础设施会带来显著隐私风险，可能暴露敏感用户数据。本文提出一种新型隐私保护卸载框架，结合联邦学习（FL）与差分隐私（DP）技术，在保障性能的同时保护用户数据。框架包含一种考虑隐私约束与系统效率的隐私感知任务划分算法，并设计安全通信协议用于传输模型更新与聚合结果。实验表明，该方法在非隐私保护基准上仅下降2-3%准确率的情况下达到75%全局准确率，满足ε=0.8的差分隐私保证；每轮通信开销约2.1MB，计算耗时占总处理时间90%以上，验证了其在资源受限车载环境中的高效性。
原文摘要 · Abstract (English)
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.
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