  In this paper, we investigate the content popularity prediction problem in
cache-enabled fog radio access networks (F-RANs). In order to predict the
content popularity with high accuracy and low complexity, we propose a Gaussian
process based regressor to model the content request pattern. Firstly, the
relationship between content features and popularity is captured by our
proposed model. Then, we utilize Bayesian learning to train the model
parameters, which is robust to overfitting. However, Bayesian methods are
usually unable to find a closed-form expression of the posterior distribution.
To tackle this issue, we apply a stochastic variance reduced gradient
Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior
distribution. To utilize the computing resources of other fog access points
(F-APs) and to reduce the communications overhead, we propose a quantized
federated learning (FL) framework combining with Bayesian learning. The
quantized federated Bayesian learning framework allows each F-AP to send
gradients to the cloud server after quantizing and encoding. It can achieve a
tradeoff between prediction accuracy and communications overhead effectively.
Simulation results show that the performance of our proposed policy outperforms
the existing policies.
