  The usage of unmanned aerial vehicles (UAVs) in civil and military
applications continues to increase due to the numerous advantages that they
provide over conventional approaches. Despite the abundance of such advantages,
it is imperative to investigate the performance of UAV utilization while
considering their design limitations. This paper investigates the deployment of
UAV swarms when each UAV carries a machine learning classification task. To
avoid data exchange with ground-based processing nodes, a federated learning
approach is adopted between a UAV leader and the swarm members to improve the
local learning model while avoiding excessive air-to-ground and ground-to-air
communications. Moreover, the proposed deployment framework considers the
stringent energy constraints of UAVs and the problem of class imbalance, where
we show that considering these design parameters significantly improves the
performances of the UAV swarm in terms of classification accuracy, energy
consumption and availability of UAVs when compared with several baseline
algorithms.

Machine Learning;Signal Processing