Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

2020/02/13 by Yunlong Lu, Xiaohong Huang, Ke Zhang +2 · 734 citations
Computer Science · Engineering · #Artificial intelligence #Asynchronous communication #Blockchain #Blockchain Technology Applications and Security #Computer network #Computer science #Computer security #Data sharing #Distributed computing #Engineering #Information privacy #Node (physics) #Privacy-Preserving Technologies in Data #Reinforcement learning #Reliability (semiconductor) #The Internet #Vehicular Ad Hoc Networks (VANETs) #World Wide Web

paper · doi:10.1109/tvt.2020.2973651

published in IEEE Transactions on Vehicular Technology 69(4), 4298-4311 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2020/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.

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