  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.
