  Backhaul traffic congestion caused by the video traffic of a few popular
files can be alleviated by storing the to-be-requested content at various
levels in wireless video caching networks. Typically, content service providers
(CSPs) own the content, and the users request their preferred content from the
CSPs using their (wireless) internet service providers (ISPs). As these parties
do not reveal their private information and business secrets, traditional
techniques may not be readily used to predict the dynamic changes in users'
future demands. Motivated by this, we propose a novel resource-aware
hierarchical federated learning (RawHFL) solution for predicting user's future
content requests. A practical data acquisition technique is used that allows
the user to update its local training dataset based on its requested content.
Besides, since networking and other computational resources are limited,
considering that only a subset of the users participate in the model training,
we derive the convergence bound of the proposed algorithm. Based on this bound,
we minimize a weighted utility function for jointly configuring the
controllable parameters to train the RawHFL energy efficiently under practical
resource constraints. Our extensive simulation results validate the proposed
algorithm's superiority, in terms of test accuracy and energy cost, over
existing baselines.
