Federated learning approach for human activity recognition in online examination environment

Abstract

In recent years, online exams have become a key method for assessing students’ knowledge and skills. However, with the rise of e-learning, conducting these exams has introduced new challenges, especially due to the increasing tendency of students to engage in cheating during online assessments. To address this, student activities during online exams are monitored to detect behaviors that indicate cheating through Human Activity Recognition (HAR). HAR is a system capable of recognizing various human activities based on observational data. This study focuses on detecting student behavior during online exams, categorizing normal activities as non-cheating and abnormal activities as potential cheating or malpractice. For this purpose, a federated learning architecture was utilized to process online exam data. In this approach, we implemented federated models, including Federated-ResNet50, Federated-DenseNet121, Federated-VGG16, and Federated-CNN, to classify student activities. A OEP dataset is utilized in this work comprising of various activities such as using mobile devices, copying from notes, abnormal head gaze and normal. Model performance for classification was evaluated using accuracy, precision, recall and F1-Score metrics. The results were compared across the federated models, namely Federated-ResNet50, Federated-DenseNet121, Federated-VGG16, and Federated-CNN. Among these, Federated-ResNet50 performed the best, achieving an accuracy of 91.28%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.