TY - JOUR

T1 - Asynchronous Federated Learning for Vehicular Edge Caching of Consumer Content

AU - Xu, Xiaolong

AU - Bao, Guanming

AU - Bilal, Muhammad

PY - 2024/1/24

Y1 - 2024/1/24

N2 - Edge caching is one of the key technologies, which enhances consumer content delivery and reduces service latency by caching consumer content and services on edge nodes. Learning-based caching algorithms have been proposed in the literature to achieve high caching efficiency. However, in the complicated task of edge caching for consumer content in vehicular networks, it is challenging to achieve a high cache hit rate and low consumer content delivery delay. This paper proposes AFRL, an Asynchronous Federated Learning with Deep Reinforcement Learning edge consumer content caching algorithm for vehicular networks. AFRL utilizes federated learning to collaboratively train a shared DRL agent among Roadside Units(RSUs), and an efficient asynchronous federated learning algorithm is also introduced to accelerate convergence and improve cache hit rates in dynamic environments. Simulation results demonstrate the superior performance of AFRL compared to traditional and state-of-the-art caching algorithms, showcasing its potential in handling varying traffic densities, achieving higher cache hit rates, and low consumer content delivery delay.

AB - Edge caching is one of the key technologies, which enhances consumer content delivery and reduces service latency by caching consumer content and services on edge nodes. Learning-based caching algorithms have been proposed in the literature to achieve high caching efficiency. However, in the complicated task of edge caching for consumer content in vehicular networks, it is challenging to achieve a high cache hit rate and low consumer content delivery delay. This paper proposes AFRL, an Asynchronous Federated Learning with Deep Reinforcement Learning edge consumer content caching algorithm for vehicular networks. AFRL utilizes federated learning to collaboratively train a shared DRL agent among Roadside Units(RSUs), and an efficient asynchronous federated learning algorithm is also introduced to accelerate convergence and improve cache hit rates in dynamic environments. Simulation results demonstrate the superior performance of AFRL compared to traditional and state-of-the-art caching algorithms, showcasing its potential in handling varying traffic densities, achieving higher cache hit rates, and low consumer content delivery delay.

KW - Electrical and Electronic Engineering

KW - Computer Science Applications

KW - Hardware and Architecture

KW - Human-Computer Interaction

U2 - 10.1109/mce.2024.3358025

DO - 10.1109/mce.2024.3358025

M3 - Journal article

SP - 1

EP - 6

JO - IEEE Consumer Electronics Magazine

JF - IEEE Consumer Electronics Magazine

SN - 2162-2248

ER -