Edge Resource

Edge resource management focuses on optimizing the deployment and utilization of computational resources at the network's edge for AI applications, particularly deep learning models like Large Language Models (LLMs) and Graph Neural Networks (GNNs). Current research emphasizes efficient resource allocation strategies, often employing game theory, reinforcement learning, and neural architecture search (NAS) to address challenges like limited bandwidth, power constraints, and heterogeneous device capabilities. These advancements are crucial for enabling privacy-preserving, low-latency AI services in diverse applications, from smart cities to mobile devices, by improving both the speed and energy efficiency of edge computing.