Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networks

Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users’ requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users’ personal information. Traditional federated learning (FL) can protect users’ privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then a popular content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.

I. Introduction

In recent years, with the increasing popularity of smart devices, we have witnessed an unprecedented growth in mobile data traffic, which has imposed a heavy burden on wireless networks [1], [2], [3]. As users increasingly rely on user equipment (UEs) like mobile devices and home routers to access content from wireless networks, it becomes challenging for ensuring a satisfactory quality of service to meet their demands [4], [5], [6]. To address this challenge, edge caching has emerged as a promising solution for next-generation networks [7], [8], [9]. Through the implementation of caching units in wireless edge nodes, such as small-cell base stations (SBSs), UEs can fetch users’ desired contents from nearby SBSs instead of remote servers or cloud. This process significantly reduces traffic load, alleviates network congestion, reduces latency and improves system performance [10].