Federated Learning-based Real-Time Participatory Road Condition Monitoring System

The road networks serve as the backbone of modern societies. Road damages such as potholes account for a significant portion of road accidents and vehicle damage. The advent of IoT-enabled smartphones and advancements in 6G communication technology enables efficient real-time data collection from road users. Existing pothole detection systems often rely on centralised approaches to train models, compromising users privacy which can potentially drive users from participating in these systems. In this paper, we propose a Federated Learning (FL) framework to detect potholes using ubiquitous smartphones for sensory data collection, while ensuring user’s sensitive data privacy. To train the models on the participating user’s smartphones, we implement three approaches: deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM), as potential choices for local models, to address the diversity in resource availability among different smartphones. The experiments on a real-world dataset show that all the proposed approaches achieve satisfactory performance compared to existing methods while being cost-effective, privacy preserving, and computationally efficient. The proposed system supported by 6G infrastructure, enables large-scale real-time pothole detection by warning drivers about upcoming potholes.