Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …

In this paper, we investigate the joint impact of task priority and mobile computing service on
the mobile edge computing (MEC) networks, in which one unmanned aerial vehicle (UAV) …

Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends
requests to gather private data from an edge-computing-based IoT cloud storage system …

Opportunistic computation offloading is an effective method to improve the computation
performance of mobile-edge computing (MEC) networks under dynamic edge environment …

In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …

Mobile edge computing (MEC) is an emergent architecture, which brings computation and
storage resources to the edge of mobile network and provides rich services and applications …

Computational offloading, as an effective way to extend the capability of resource-limited
edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for …

This paper studies a secure mobile edge computing (MEC) for emerging cyber physical
systems (CPS), where there exist K eavesdroppers in the network, which can threaten the …

Today, cloud computation offloading may not be an appropriate solution for delay-sensitive
applications due to the long distance between end-devices and remote datacenters. In …