61 research outputs found
Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks
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.Comment: This paper has been accepted by TW
Alleviating Distortion Accumulation in Multi-Hop Semantic Communication
Recently, semantic communication has been investigated to boost the
performance of end-to-end image transmission systems. However, existing
semantic approaches are generally based on deep learning and belong to lossy
transmission. Consequently, as the receiver continues to transmit received
images to another device, the distortion of images accumulates with each
transmission. Unfortunately, most recent advances overlook this issue and only
consider single-hop scenarios, where images are transmitted only once from a
transmitter to a receiver. In this letter, we propose a novel framework of a
multi-hop semantic communication system. To address the problem of distortion
accumulation, we introduce a novel recursive training method for the encoder
and decoder of semantic communication systems. Specifically, the received
images are recursively input into the encoder and decoder to retrain the
semantic communication system. This empowers the system to handle distorted
received images and achieve higher performance. Our extensive simulation
results demonstrate that the proposed methods significantly alleviate
distortion accumulation in multi-hop semantic communication
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