Phong, L.T., Aono, Y., Hayashi, T., Wang, L. and Moriai, S. (2018) Privacy Preserving Deep Learning via Additively Homomorphic Encryption. IEEE Transactions on Information Forensics and Security, 13, 1333-1345.
https://doi.org/10.1109/TIFS.2017.2787987

has been cited by the following article:

• TITLE: Decentralized Semi-Supervised Learning for Stochastic Configuration Networks Based on the Mean Teacher Method

  AUTHORS: Kaijing Li, Wu Ai

  KEYWORDS: Stochastic Neural Network, Consistency Regularization, Semi-Supervised Learning, Decentralized Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.4, April 30, 2024

  ABSTRACT: The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.