  Video caching can significantly improve backhaul traffic congestion by
locally storing the popular content that users frequently request. A
privacy-preserving method is desirable to learn how users' demands change over
time. As such, this paper proposes a novel resource-aware hierarchical
federated learning (RawHFL) solution to predict users' future content requests
under the realistic assumptions that content requests are sporadic and users'
datasets can only be updated based on the requested content's information.
Considering a partial client participation case, we first derive the upper
bound of the global gradient norm that depends on the clients' local training
rounds and the successful reception of their accumulated gradients over the
wireless links. Under delay, energy and radio resource constraints, we then
optimize client selection and their local rounds and central processing unit
(CPU) frequencies to minimize a weighted utility function that facilitates
RawHFL's convergence in an energy-efficient way. Our simulation results show
that the proposed solution significantly outperforms the considered baselines
in terms of prediction accuracy and total energy expenditure.
