  It is anticipated that aerial-terrestrial integrated networks incorporating
unmanned aerial vehicles (UAVs) mounted relays will offer improved coverage and
connectivity in the beyond 5G era. Meanwhile, federated learning (FL) is a
promising distributed machine learning technique for building inference models
over wireless networks due to its ability to maintain user privacy and reduce
communication overhead. However, off-the-shelf FL models aggregate global
parameters at a central parameter server (CPS), increasing energy consumption
and latency, as well as inefficiently utilizing radio resource blocks (RRBs)
for distributed user devices (UDs). This paper presents a resource-efficient FL
framework, called FedMoD (\textbf{fed}erated learning with \textbf{mo}del
\textbf{d}issemination), for millimeter-wave (mmWave) aerial-terrestrial
integrated networks with the following two unique characteristics. Firstly,
FedMoD presents a novel decentralized model dissemination algorithm that makes
use of UAVs as local model aggregators through UAV-to-UAV and device-to-device
(D2D) communications. As a result, FedMoD (i) increases the number of
participant UDs in developing FL model and (ii) achieves global model
aggregation without involving CPS. Secondly, FedMoD reduces the energy
consumption of FL using radio resource management (RRM) under the constraints
of over-the-air learning latency. In order to achieve this, by leveraging graph
theory, FedMoD optimizes the scheduling of line-of-sight (LOS) UDs to suitable
UAVs/RRBs over mmWave links and non-LOS UDs to available LOS UDs via overlay
D2D communications. Extensive simulations reveal that decentralized FedMoD
offers same convergence rate performance as compared to conventional FL
frameworks.
