LLM-Empowered IoT for 6G Networks: Architecture, Challenges, and Solutions
Xiaopei Chen, Wen Wu, Liang Li, Fei Ji
Abstract
The Internet of Things (IoT) in the sixth generation (6G) network is envisioned to evolve towards intelligence, ubiquity, and self-optimization. Large language models (LLMs) have demonstrated remarkable generalization capabilities across diverse domains, including natural language processing, computer vision, etc. In this article, we propose an LLM-empowered IoT architecture for 6G networks to achieve intelligent autonomy and facilitate diversified IoT applications. On one hand, LLM-based solutions are tailored to satisfy the quality of service requirements of IoT applications, i.e., LLM for 6G IoT. On the other hand, LLMs are expected to be deployed in IoT environments through edge fine-tuning methods and edge inference techniques to support IoT applications, i.e., LLM on 6G IoT. Furthermore, we propose a memory-efficient split federated learning framework for LLM fine-tuning on heterogeneous IoT devices to alleviate memory consumption on both IoT devices and the edge server. Finally, a case study is presented, followed by a discussion about open issues.