A Federated Learning Based Connected Vehicular Framework for Smart Health Care

Data privacy and data security are the main concerns in the digital era. The 3.5% centralized increase in annual digital data and the use of machine learning and deep learning approaches in the centralized computing environment endanger data privacy and security. The evolution of various body sensors also increases the digital health parameter data, which also demands privacy and security, which are difficult to achieve in a centralized computing environment. In the article, a two-level VANET-based federated learning framework is proposed for the classification of health parameters, in which Road Side Unit (RSU) acts as the local server in the first level and cloud networks act as the remote server in the second level. Health parameters considered for the proposed work are body temperature, heart rate, and systolic and diastolic blood pressure. The proposed work classifies health parameters into four categories, such as normal, low-risk, medium-risk, and high-risk data. Accuracy, precision, recall, and loss are used to evaluate the proposed work. In addition, in terms of average false classification rate and multi-class classification accuracy, the proposed federated learning computing is compared to centrally managed computing.