Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep
  Reinforcement Learning in Next-Generation Network
Qiong Wu Wenhua Wang Pingyi Fan Qiang Fan Huiling Zhu Khaled B. Letaief
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.