Performance Evaluation of Hierarchical Federated Learning Networks Based on Stochastic Network Calculus

Abstract

Analyzing the key factors affecting the delay of hierarchical federated learning and reducing the generation of delay is an important issue to be addressed. In this paper, we analyze the hierarchical federated learning network in the case of simultaneous access of mobile devices and model the arrival process and service process of data streams satisfying Poisson distribution. This paper analyzes the delay bound of the hierarchical federated learning network under a round of global updates using stochastic network calculus. We model a more realistic service model by considering the service rate variation of edge servers due to channel fading and other factors when analyzing the delay bound of the wireless access network. Finally, we analyze the parameters affecting the end-to-end delay performance of the hierarchical federated learning network in numerical analysis. The factors that affect the latency are the number of mobile nodes, the number of edge nodes, and the arrival rate of the data flow.