FedMCRNN: Federated Learning using Multiple Convolutional Recurrent Neural Networks for Sleep Quality Prediction

Abstract

"Good night"is the most common saying everyone uses every day. That shows sleep plays a vital role in human life, and about a third of a lifetime is spent sleeping. Having a good sleep means having good health, spirit, and intellect to work. Many studies have analyzed predicted sleep quality using machine learning (ML). However, no studies have federated learning (FL) to analyze and predict sleep quality predictions. Our study operated federated multiple convolutional neural networks (FedMCRNN) and multi-modal data collected from wearables for sleep quality prediction. We measure the performance of the FedMCRNN in many-To-one and many-To-many cases using a variety of metrics and compare it with traditional machine learning models. The results show that FedMCRNN predicts quality intention reliably, with 96.774% and 68.721% accuracies for the two cases, many-To-one and many-To-many, respectively. Besides, other metrics have better value than methods. The results also show that FedMCRNN performs better than previous most advanced methods for predicting sleep quality and clearly shows which features influence sleep quality. Our findings have implications for the development of Artificial Intelligence (AI) doctors.