  This paper investigates the control of a massive population of UAVs such as
drones. The straightforward method of control of UAVs by considering the
interactions among them to make a flock requires a huge inter-UAV communication
which is impossible to implement in real-time applications. One method of
control is to apply the mean-field game (MFG) framework which substantially
reduces communications among the UAVs. However, to realize this framework,
powerful processors are required to obtain the control laws at different UAVs.
This requirement limits the usage of the MFG framework for real-time
applications such as massive UAV control. Thus, a function approximator based
on neural networks (NN) is utilized to approximate the solutions of
Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations.
Nevertheless, using an approximate solution can violate the conditions for
convergence of the MFG framework. Therefore, the federated learning (FL)
approach which can share the model parameters of NNs at drones, is proposed
with NN based MFG to satisfy the required conditions. The stability analysis of
the NN based MFG approach is presented and the performance of the proposed
FL-MFG is elaborated by the simulations.
