Apr 7, 2021 · In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive ...

In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive ...

PrivateSNN. Pytorch Implementation for [PrivateSNN: Privacy-Preserving Spiking Neural Networks]. Accepted in AAAI2022. Prerequisites. Python 3.8; PyTorch 1.5.0 ...

This paper proposes PrivateSNN, which aims to build low-power Spiking Neural Networks from a pre-trained ANN model without leaking sensitive information ...

Abstract: How can we bring both privacy and energy-efficiency to a neural system on edge devices? In this paper, we propose PrivateSNN, which aims to build ...

Apr 7, 2021 · How can we bring both privacy and energy-efficiency to a neural system on edge devices? In this paper, we propose PrivateSNN, which aims to ...

These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic ...

Sep 10, 2024 · The third-generation neural network, SNN (Spiking Neural Network), mimics real neurons by using discrete spike signals, whose sequences exhibit ...

Nov 20, 2023 · By incorporating noise into the gradients of both ANNs and SNNs, privacy protection without compromising network performance can be enhanced.

May 7, 2024 · Panda, “Privatesnn: Privacy-preserving spiking neural networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no ...