Sleep Staging Method for Imbalanced EEG Data Based on Differential Privacy Federated Learning

Abstract

EEG is a personal privacy information, that is difficult to share data and not conducive to the study of sleep staging. In this paper, the Federated Learning (FL) method is introduced to solve the problem of data island in sleep staging EEG signals. To simulate the distribution of different user data, we introduce the K-means algorithm into EEG dataset partitioning and transform it into a non-independent and identically distributed unbalanced dataset. Then, based on the classic Multilayer Perceptron (MLP) network, we analyse the privacy leakage problem in FL, and Differential Privacy (DP) is used to protect the privacy of user EEG data. Next, we analyse the reason for the accuracy loss between FL and Centralized Learning (CL), and the effect of the imbalance of the data distribution of each client. We propose a self-adaptive batch method to adaptively balance the batch size of each client, and experiments show that the training results of imbalanced datasets can be improved. Finally, when the accuracy of the model does not reach the historical best value in the set epoch interval, the Mandatory Optimization Strategy (MOS) is used to further improve the accuracy and reduce the randomness of the model. The DP-FL based on the self-adaptive batch and MOS strategy is tested on our dataset and the Sleep-EDF Database Expanded dataset, which not only protects user privacy but also achieves the same accuracy as CL. The accuracy is 82.88% on our dataset and 91.81% on the subset of the Sleep-EDF Database Expanded dataset.