Personalized Privacy-Aware MARL Offloading in the Edge-Cloud Continuum

Abstract

Task offloading in End–Edge–Cloud computing enables resource-constrained User Devices (UDs) to execute computation-intensive applications with reduced latency and energy consumption. However, existing task offloading schemes generally overlook the heterogeneous privacy requirements of users, leading to inefficient privacy-aware offloading decisions. In this paper, we propose a personalized privacy-aware task offloading framework, where the privacy requirement of each task is quantified through a User-Centric Privacy (UCP) model that considers task sensitivity, user privacy preferences, and task urgency. The task offloading problem is formulated as a Markov Decision Process (MDP) to jointly optimize the offloading ratio and the target computing server while minimizing the long term system cost under latency, energy, and privacy constraints. To efficiently solve the resulting high-dimensional continuous optimization problem, we develop an Advantage-Weighted Actor Critic (AWAC)-enhanced Proximal Policy Optimization (PPO) based Multi-Agent Reinforcement Learning (MARL) algorithm under the Centralized Training and Decentralized Execution (CTDE) paradigm. Simulation results demonstrate that the proposed framework achieves lower average system cost, lower energy consumption, and higher task success rate than represen tative baselines under diverse privacy requirements.