Federated Graph Learning for Low Probability of Detection in Wireless
  Ad-Hoc Networks
Sivaram Krishnan Jihong Park Subhash Sagar Gregory Sherman Benjamin Campbell Jinho Choi
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.