Timely Asynchronous Hierarchical Federated Learning: Age of Convergence
Purbesh Mitra Sennur Ulukus
We consider an asynchronous hierarchical federated learning (AHFL) setting
with a client-edge-cloud framework. The clients exchange the trained parameters
with their corresponding edge servers, which update the locally aggregated
model. This model is then transmitted to all the clients in the local cluster.
The edge servers communicate to the central cloud server for global model
aggregation. The goal of each client is to converge to the global model, while
maintaining timeliness of the clients, i.e., having optimum training iteration
time. We investigate the convergence criteria for such a system with dense
clusters. Our analysis shows that for a system of $n$ clients with fixed
average timeliness, the convergence in finite time is probabilistically
guaranteed, if the nodes are divided into $O(1)$ number of clusters, that is,
if the system is built as a sparse set of edge servers with dense client bases
each.