Scalable Private P2P network for distributed and Hierarchical Machine Learning in VANETs

With the recent development of the Internet of Things (IoT), applications are becoming smarter and connected devices are being used in all aspects. As the amount of collected data increases, machine learning (ML) technology has been applied and is being used as a useful tool for extracting vast amounts of information. If the data set is wide and distributed, old machine learning algorithms cannot be used because the whole training data should be centralized in one location. Therefore, distributed learning, federated learning, and circular learning are being used. In this paper, we propose a new Trust-based Edge network architecture that is suitable for distributed learning and hierarchical machine learning in a Vehicular ad-hoc network(VANETs) it is inspired by Dempster-Shafer theory with Scalable Chord Peer to Peer Network. In order to cut down on computation, communication costs, and time.