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.

中文翻译：

---

通信高效的大规模无人机在线路径控制：联邦学习遇见平均场博弈论（特邀论文）

本文研究了对大量无人机（例如无人机）的控制。通过考虑无人机之间的相互作用来控制无人机的直接方法需要大量的无人机间通信，这在实时应用中是不可能实现的。一种控制方法是应用平均场博弈 (MFG) 框架，该框架大大减少了无人机之间的通信。然而，要实现这个框架，需要强大的处理器来获取不同无人机的控制规律。这一要求限制了 MFG 框架在大规模无人机控制等实时应用中的使用。因此，基于神经网络 (NN) 的函数逼近器用于逼近 Hamilton-Jacobi-Bellman (HJB) 和 Fokker-Planck-Kolmogorov (FPK) 方程的解。尽管如此，使用近似解可能违反 MFG 框架收敛的条件。因此，提出了可以在无人机上共享 NN 模型参数的联邦学习 (FL) 方法，该方法使用基于 NN 的 MFG 来满足所需条件。介绍了基于 NN 的 MFG 方法的稳定性分析，并通过仿真详细说明了所提出的 FL-MFG 的性能。