  Unmanned aerial vehicle (UAV) swarms are considered as a promising technique
for next-generation communication networks due to their flexibility, mobility,
low cost, and the ability to collaboratively and autonomously provide services.
Distributed learning (DL) enables UAV swarms to intelligently provide
communication services, multi-directional remote surveillance, and target
tracking. In this survey, we first introduce several popular DL algorithms such
as federated learning (FL), multi-agent Reinforcement Learning (MARL),
distributed inference, and split learning, and present a comprehensive overview
of their applications for UAV swarms, such as trajectory design, power control,
wireless resource allocation, user assignment, perception, and satellite
communications. Then, we present several state-of-the-art applications of UAV
swarms in wireless communication systems, such us reconfigurable intelligent
surface (RIS), virtual reality (VR), semantic communications, and discuss the
problems and challenges that DL-enabled UAV swarms can solve in these
applications. Finally, we describe open problems of using DL in UAV swarms and
future research directions of DL enabled UAV swarms. In summary, this survey
provides a comprehensive survey of various DL applications for UAV swarms in
extensive scenarios.
