Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model's accuracy, under UAV constraints of energy consumption, communication quality, and local datasets' heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.