A privacy-preserving student status monitoring system

Abstract

Timely feedback of students’ listening status is crucial for teaching work. However, it is often difficult for teachers to pay attention to all students at the same time. By leveraging surveillance cameras in the classroom, we are able to assist the teaching work. However, the existing methods either lack the protection of students’ privacy, or they have to reduce the accuracy of success, because they are concerned about the leakage of students’ privacy. We propose federated semi-supervised class assistance system to evaluate the listening status of students in the classroom. Rather than training the semi-supervised model in a centralized manner, we train a semi-supervised model in a federated manner among various monitors while preserving students’ privacy. We also formulate a new loss function according to the difference between the pre-trained initial model and the expected model to restrict the training process of the unlabeled data. By applying the pseudo-label assignment method on the unlabeled data, the class monitors are able to recognize the student class behavior. In addition, simulation and real-world experimental results demonstrate that the performance of the proposed system outperforms that of the baseline models.