WITHDRAWN: Towards efficient and reliable federated learning using Blockchain for autonomous vehicles
Abstract Federated learning (FL) has emerged as a robust privacy aware decentralized computing approach for personalized data in a network of nodes. It has the potential to be the boon for future autonomous vehicles. FL can significantly improve performance while ensuring privacy preservation for all vehicles. It can exploit distributed set of training data while maintaining robust local learning within each vehicle. However, standard FL incurs long communication delays and risk of failures due to a single global server used for aggregating local learning. Any unreliable model can be uploaded by an adversary vehicle (if any) leads to deception in the learning of the whole FL system. Some fraud vehicles may often perform unreliable updates intentionally, e.g. the data poisoning attack, or unintentionally, e.g. low quality data caused by energy constraints or high speed 5G mobility. Therefore, investigation of efficient and trustworthy FL for autonomous vehicles becomes critical and is a challenging task. Therefore, we design a lightweight multilevel Blockchain framework for improving the end to end trustworthiness of the FL system of Internet of Vehicles (IoV) At the heart of the solution is the integration of trustworthiness and reputation modules, which not only learns and jointly evaluates the trustworthiness of vehicle's observations during data collection but also adapts prompt block verification at the Blockchain level.