# A Federated Channel Modeling System using Generative Neural Networks

arXiv: https://arxiv.org/abs/2305.18856 · HTML: https://mlsanity.com/paper/2305.18856/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication](https://mlsanity.com/paper/2012.09133/) (2020)
- [Federated Feature Selection for Cyber-Physical Systems of Systems](https://mlsanity.com/paper/2109.11323/) (2021)
- [Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey](https://mlsanity.com/paper/2107.06881/) (2021)
- [Millimeter Wave Channel Modeling via Generative Neural Networks](https://mlsanity.com/paper/2008.11006/) (2020)
- [Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks](https://mlsanity.com/paper/2205.00550/) (2022)

## Abstract (arXiv)

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