Energy-Privacy Tradeoff for Task Matching in Edge Computing Power Networks

The sixth-generation (6 G) networks aim to achieve ubiquitous intelligent connectivity while ensuring extremely low latency, reducing energy consumption, and enhancing privacy protection. Mobile edge computing (MEC) offers an effective solution to reduce latency and energy consumption by leveraging resources near end devices for task offloading. However, MEC faces significant challenges in meeting the requirements of 6 G networks, including limited computational resources, high mobility, and strict data privacy demands. Efficiently allocating edge resources while preserving privacy has become a critical issue for realizing the objectives of 6 G networks. In this paper, we propose a privacy-preserving edge computing power network (EdgeCPN) model that jointly leverages the computing resources of edge computing nodes and protects sensitive computing power information through differential privacy methods. In addition, we propose a task matching problem that aims to minimize the privacy-budget-weighted energy consumption while ensuring privacy protection and meeting task requirements. We propose a dynamic graph-based multiagent reinforcement learning (MADRL) algorithm to find the optimal strategy for task matching and computing resource allocation with privacy protection. The results show that our proposed task matching model with energy and privacy tradeoffs can minimize the energy consumption in the matching process while ensuring privacy, and the algorithm can find the optimal strategy for task matching efficiently.