FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications

The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.