  Multi-access edge computing (MEC) is seen as a vital component of forthcoming
6G wireless networks, aiming to support emerging applications that demand high
service reliability and low latency. However, ensuring the ultra-reliable and
low-latency performance of MEC networks poses a significant challenge due to
uncertainties associated with wireless links, constraints imposed by
communication and computing resources, and the dynamic nature of network
traffic. Enabling ultra-reliable and low-latency MEC mandates efficient load
balancing jointly with resource allocation. In this paper, we investigate the
joint optimization problem of offloading decisions, computation and
communication resource allocation to minimize the expected weighted sum of
delivery latency and energy consumption in a non-orthogonal multiple access
(NOMA)-assisted MEC network. Given the formulated problem is a mixed-integer
non-linear programming (MINLP), a new multi-agent federated deep reinforcement
learning (FDRL) solution based on double deep Q-network (DDQN) is developed to
efficiently optimize the offloading strategies across the MEC network while
accelerating the learning process of the Internet-of-Thing (IoT) devices.
Simulation results show that the proposed FDRL scheme can effectively reduce
the weighted sum of delivery latency and energy consumption of IoT devices in
the MEC network and outperform the baseline approaches.
