Privacy preservation for federated learning based on Gaussian noise scrambling

Abstract

This article discusses a privacy preservation for federated learning based on gaussian noise scrambling. The main objective of this model is to protect user data privacy and security by aggregating model parameters from multiple parties instead of raw data sets. However, attackers may still obtain sensitive information from the model parameter information transmitted during federated learning training through certain means. To address this issue, we propose a differential privacy noise addition scheme for federated learning. By adding noise to transmitted model parameters to some extent, it prevents attackers from inferring participant information through reverse inference. We also study Gaussian noise mechanism in differential privacy protection and prove it by combining characteristics of federated learning and blockchain technology for adding Gaussian noise size according to differential privacy requirements. This article tests their proposed differential privacy protection mechanism using the NSL-KDD dataset and finds that after adding differential privacy protection, models still have good anomaly recognition performance. This study contributes to the field of federated learning by proposing a novel approach for protecting user data privacy and security during training. The proposed approach can be used in various applications such as healthcare, finance, and social media where data privacy is crucial.