Privacy-Preserving Machine Learning for Snoring Detection

Snoring is one of the most important health problems. It could be a sign of sleep apnea which leads to a potentially serious sleep disorder. Thus, detecting snoring sounds is the first step to diagnosing sleep disorders. Due to improving edge device performance, we can use edge devices, or mobile phones, to detect snoring during sleep. Edge devices allow us to deploy a machine learning model, which is a widely used technique to detect snoring. However, snoring sounds are privacy-sensitive data because they contain activity information through participants’ sleep. This paper aims to detect snoring sounds on edge devices while preserving data privacy. We designed and implemented a convolutional-based neural network model that utilizes fewer commuting resources while maintaining the model’s accuracy. The proposed model contains six two-dimensional convolutional and two fully connected layers. As a result, the accuracy is improved by 1.96% and the model size is reduced by 66%. Due to the smaller model size, the proposed model is able to maintain the model accuracy with lower required communication cost for federated learning when compared to the original accuracy.