  Federated Learning is a modern decentralized machine learning technique where
user equipments perform machine learning tasks locally and then upload the
model parameters to a central server. In this paper, we consider a 3-layer
hierarchical federated learning system which involves model parameter exchanges
between the cloud and edge servers, and the edge servers and user equipment. In
a hierarchical federated learning model, delay in communication and computation
of model parameters has a great impact on achieving a predefined global model
accuracy. Therefore, we formulate a joint learning and communication
optimization problem to minimize total model parameter communication and
computation delay, by optimizing local iteration counts and edge iteration
counts. To solve the problem, an iterative algorithm is proposed. After that, a
time-minimized UE-to-edge association algorithm is presented where the maximum
latency of the system is reduced. Simulation results show that the global model
converges faster under optimal edge server and local iteration counts. The
hierarchical federated learning latency is minimized with the proposed
UE-to-edge association strategy.
