Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions

Edge computing has emerged as a vital paradigm for processing data near its source, significantly reducing latency and improving data privacy. Simultaneously, large language models (LLMs) such as GPT-4 and BERT have showcased impressive capabilities in data analysis, natural language processing, and decision-making. This survey explores the intersection of these two domains, specifically focusing on the adaptation and optimization of LLMs for data analysis tasks in edge computing environments. We examine the challenges faced by resource-constrained edge devices, including limited computational power, energy efficiency, and network reliability. Additionally, we discuss how recent advancements in model compression, distributed learning, and edge-friendly architectures are addressing these challenges. Through a comprehensive review of the current research, we analyze the applications, challenges, and future directions of deploying LLMs in edge computing. This analysis aims to facilitate intelligent data analysis across various industries, including healthcare, smart cities, and the internet of things.