Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks

TL;DR

This paper develops a new intermittent federated learning model for cellular-connected UAV networks, analyzing how communication outages and UAV deployment affect learning performance.

Contribution

It introduces an analytically tractable framework for modeling communication outages in UAV-based federated learning and evaluates its impact through simulations.

Findings

Uplink outage probability significantly affects FL performance.

UAV deployment influences the effectiveness of federated learning.

Simulation results match analytical predictions, validating the model.

Abstract

Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent FL model over a cellular-connected unmanned aerial vehicle (UAV) network, which characterizes communication outage from UAV (clients) to their server and data heterogeneity among the datasets at UAVs. We propose an analytically tractable framework to derive the uplink outage probability and use it to devise a simulation-based approach so as to evaluate the performance of the proposed intermittent FL model. Our findings reveal how the intermittent FL model is impacted by uplink communication outage and UAV deployment. Extensive numerical simulations are provided to show the consistency between the simulated and analytical performances of the proposed intermittent FL model.