Mobile Edge Computing

Mobile Edge Computing (MEC) aims to reduce latency and energy consumption by bringing computation closer to mobile devices, addressing the limitations of cloud-based processing for resource-intensive applications. Current research heavily focuses on optimizing resource allocation and task offloading using techniques like deep reinforcement learning, federated learning, and graph neural networks, often incorporating novel model architectures such as parameter-efficient fine-tuning and split learning for improved efficiency and privacy. The advancements in MEC are significant for enabling real-time applications like augmented reality, virtual reality, and the metaverse, while also driving innovation in areas such as AI model deployment and privacy-preserving machine learning.