  The paper proposes a data-driven approach to air-to-ground channel estimation
in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike
traditional centralized learning methods that are specific to certain
geographical areas and inappropriate for others, we propose a generalized model
that uses Federated Learning (FL) for channel estimation and can predict the
air-to-ground path loss between a low-altitude platform and a terrestrial
terminal. To this end, our proposed FL-based Generative Adversarial Network
(FL-GAN) is designed to function as a generative data model that can learn
different types of data distributions and generate realistic patterns from the
same distributions without requiring prior data analysis before the training
phase. To evaluate the effectiveness of the proposed model, we evaluate its
performance using Kullback-Leibler divergence (KL), and Wasserstein distance
between the synthetic data distribution generated by the model and the actual
data distribution. We also compare the proposed technique with other generative
models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN
models. The results of the study show that the synthetic data generated by
FL-GAN has the highest similarity in distribution with the real data. This
shows the effectiveness of the proposed approach in generating data-driven
channel models that can be used in different regions
