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The paper proposes a data-driven approach to air-to-ground channel estimationin a millimeter-wave wireless network on an unmanned aerial vehicle. Unliketraditional centralized learning methods that are specific to certaingeographical areas and inappropriate for others, we propose a generalized modelthat uses Federated Learning (FL) for channel estimation and can predict theair-to-ground path loss between a low-altitude platform and a terrestrialterminal. To this end, our proposed FL-based Generative Adversarial Network(FL-GAN) is designed to function as a generative data model that can learndifferent types of data distributions and generate realistic patterns from thesame distributions without requiring prior data analysis before the trainingphase. To evaluate the effectiveness of the proposed model, we evaluate itsperformance using Kullback-Leibler divergence (KL), and Wasserstein distancebetween the synthetic data distribution generated by the model and the actualdata distribution. We also compare the proposed technique with other generativemodels, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GANmodels. The results of the study show that the synthetic data generated byFL-GAN has the highest similarity in distribution with the real data. Thisshows the effectiveness of the proposed approach in generating data-drivenchannel models that can be used in different regions

A Federated Channel Modeling System using Generative Neural Networks