Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks
This work addresses performance degradation in FL for UAV networks due to communication outages, but it is incremental as it builds on existing FL frameworks with a focus on modeling and analysis.
The paper tackled the problem of communication outages in federated learning over wireless networks by proposing an intermittent FL model for cellular-connected UAV networks, and derived uplink outage probability to evaluate its performance, showing consistency between simulated and analytical results.
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.