2024 journal article
Privacy-Preserving Decentralized Inference With Graph Neural Networks in Wireless Networks
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 23(1), 543–558.
author keywords: Artificial intelligence; graph neural network; decentralized inference; differential privacy; over-the-air computation
TL;DR:
This paper adopts local differential privacy as the metric, and design novel privacy-preserving signals as well as privacy-guaranteed training algorithms to achieve privacy- Preserving inference in decentralized inference with GNNs in wireless networks.
(via Semantic Scholar)
open access via arxiv.org (repository)
Source: Web Of Science
Added: February 12, 2024
As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the inference stage of GNNs can be naturally implemented in a decentralized manner, GNN is a potential enabler for decentralized control/management in the next-generation wireless communications. Privacy leakage, however, may occur due to the information exchanges among neighbors during decentralized inference with GNNs. To deal with this issue, in this paper, we analyze and enhance the privacy of decentralized inference with GNNs in wireless networks. Specifically, we adopt local differential privacy as the metric, and design novel privacy-preserving signals as well as privacy-guaranteed training algorithms to achieve privacy-preserving inference. We also define the SNR-privacy trade-off function to analyze the performance upper bound of decentralized inference with GNNs in wireless networks. To further enhance the communication and computation efficiency, we adopt the over-the-air computation technique and theoretically demonstrate its advantage in privacy preservation. Through extensive simulations on the synthetic graph data, we validate our theoretical analysis, verify the effectiveness of proposed privacy-preserving wireless signaling and privacy-guaranteed training algorithm, and offer some guidance on practical implementation.