Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

TL;DR

This paper introduces a federated deep reinforcement learning approach for efficient computation offloading and resource allocation in fog radio access networks, reducing delay and energy use while preserving privacy.

Contribution

It proposes a novel federated DDPG algorithm tailored for dynamic F-RAN environments, enhancing efficiency and privacy in task offloading.

Findings

Lower task execution delay achieved

Reduced energy consumption of mobile devices

Faster convergence compared to existing methods

Abstract

The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.