Jun-Tao Chen

Adaptive Zero-Trust Microsegmentation via Graph-Aware Learning for Wireless Networks

Large-scale wireless networks enable dense multihop connectivity but also expose new pathways for lateral attack propagation. This paper develops a theory-guided adaptive microsegmentation framework that contains attack spread while preserving network connectivity. Grounding the framework in the zero-trust principle of...

Collaborative Cloud-Edge Computing via LLM-Guided Constrained Reinforcement Learning

Cloud computing (CC) and mobile edge computing (MEC) has become essential for augmenting computational capacity and minimizing latency by offloading tasks from user equipments (UEs) to servers. While deep reinforcement learning (DRL) is a powerful tool for optimizing offloading decisions and managing resource allocatio...

Ensembling personalized quantum models with local differential privacy

Dependency-Aware Task Offloading via Quantum Graph Attention Network-based Deep Reinforcement Learning in Collaborative Edge-Cloud Systems

Edge–cloud collaboration has become essential for managing the limited computational capacity of edge servers while meeting the dynamic and latency-critical demands of wireless devices in 5G and beyond networks. However, efficient offloading of interdependent tasks remains challenging due to heterogeneous computing res...