  Decentralized learning empowers wireless network devices to collaboratively
train a machine learning (ML) model relying solely on device-to-device (D2D)
communication. It is known that the convergence speed of decentralized
optimization algorithms severely depends on the degree of the network
connectivity, with denser network topologies leading to shorter convergence
time. Consequently, the local connectivity of real world mesh networks, due to
the limited communication range of its wireless nodes, undermines the
efficiency of decentralized learning protocols, rendering them potentially
impracticable. In this work we investigate the role of an unmanned aerial
vehicle (UAV), used as flying relay, in facilitating decentralized learning
procedures in such challenging conditions. We propose an optimized UAV
trajectory, that is defined as a sequence of waypoints that the UAV visits
sequentially in order to transfer intelligence across sparsely connected group
of users. We then provide a series of experiments highlighting the essential
role of UAVs in the context of decentralized learning over mesh networks.
