# Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks

arXiv: https://arxiv.org/abs/2306.01143 · HTML: https://mlsanity.com/paper/2306.01143/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Multi-Hop Routing in Covert Wireless Networks](https://mlsanity.com/paper/1803.04100/) (2018)
- [Low Probability of Detection Communication: Opportunities and Challenges](https://mlsanity.com/paper/1906.07895/) (2019)

## Built on by (most cited first, 2 of 2)

- [Graph Koopman Autoencoder for Predictive Covert Communication Against UAV Surveillance](https://mlsanity.com/paper/2402.09426/) (2024)
- [Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder](https://mlsanity.com/paper/2409.17048/) (2024)

## Abstract (arXiv)

Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks. Unlike existing wireless security techniques, LPD measures aim to conceal the entire existence of wireless communication instead of safeguarding the information transmitted from users. Motivated by LPD communication, in this paper, we study a privacy-preserving and distributed framework based on graph neural networks to minimise the detectability of a wireless ad-hoc network as a whole and predict an optimal communication region for each node in the wireless network, allowing them to communicate while remaining undetected from external actors. We also demonstrate the effectiveness of the proposed method in terms of two performance measures, i.e., mean absolute error and median absolute error.