Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
This work addresses the problem of delivering personalized, low-latency, and privacy-preserving AI services for users in connected cyber-physical systems, representing an incremental advancement by integrating existing techniques like task-oriented communication and federated learning with LLMs.
The paper tackles the challenge of achieving connected intelligence in wireless networks by proposing an autonomous edge AI system that leverages large language models (GPT) to automatically organize, adapt, and optimize AI services at the network edge, with experimental results showing accurate user demand comprehension, efficient model execution with minimal cost, and effective creation of high-performance models.
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world. Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence by delivering high-quality, low-latency, and privacy-preserving AI services at the network edge. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements, leveraging the power of large language models (LLMs), i.e., Generative Pretrained Transformer (GPT). By exploiting the powerful abilities of GPT in language understanding, planning, and code generation, as well as incorporating classic wisdom such as task-oriented communication and edge federated learning, we present a versatile framework that efficiently coordinates edge AI models to cater to users' personal demands while automatically generating code to train new models in a privacy-preserving manner. Experimental results demonstrate the system's remarkable ability to accurately comprehend user demands, efficiently execute AI models with minimal cost, and effectively create high-performance AI models at edge servers.