Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks. (arXiv:2208.06963v1 [cs.IT])

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 …

decentralized networks neural networks privacy wireless