FRL-Assisted Edge Service Offloading Mechanism for IoT Applications in FiWi HetNets

Abstract

To both take the advantage of wired and wireless networks, the burgeoning mobile edge computing (MEC) technology is integrated into fiber-wireless (FiWi) network to support the cost-effective deployment of Internet of Things (IoT). However, the trusted model training, efficient task computing, reasonable comprehensive energy consumption and different quality of services, are still the key problems to be solved. Thus, we introduce the federated reinforcement learning (FRL) to the framework to jointly optimize the accessing mode selection, computation offloading decision and transmission power allocation without the leakage of users’ privacy. Then, we further design a twolayer FRL algorithm based on reputation value to respectively realize the protection of user privacy and efficient optimization of the global model. The simulation results demonstrate that our proposed method outperforms others in balancing energy consumption, reducing service delay, as well as providing differentiated services.