Quantized Graph-Based Personalized DRL for Dependency-Aware Task Offloading in Heterogeneous Edge Networks

Task offloading in mobile edge computing (MEC) becomes particularly challenging when dealing with complex, interdependent subtasks, especially under dynamic network conditions and resource constraints. This challenge is central to enabling intelligent and adaptive behavior in holographic counterparts, real-time virtual replicas of physical consumer Internet of Things (IoT) devices such as smart wearables, home automation units, or augmented reality systems. In this paper, we propose HiDeR-GQ, a novel Hierarchical Dependency Reduction framework that integrates quantized graph-based deep reinforcement learning (DRL) which acts as the decision-making core of holographic counterparts deployed in heterogeneous edge networks. The core of HiDeR-GQ is a customized deep reinforcement learning agent that integrates a quantized Graph Attention Network (GATv2) for structure-aware state encoding of task dependency graphs, and a personalized federated Double Deep Q-Network (DDQN) to enable collaborative learning across distributed edge nodes while accounting for heterogeneity. To further optimize performance, we introduce a latency-aware dependency pruning mechanism that hierarchically simplifies task graphs by removing non-critical dependencies to reduce end-to-end latency without compromising task accuracy. We evaluate HiDeR-GQ in a simulated MEC environment involving multiple edge servers and users executing directed acyclic graph (DAG) structured application tasks. The results demonstrate that HiDeR-GQ achieves up to 25% lower average task completion delay, higher cumulative reward, and reduced communication cost compared to state-of-the-art offloading strategies. These benefits come with minimal trade-offs in decision optimality, highlighting HiDeR-GQ’s efficiency and scalability for next-generation IoT and edge intelligence applications.