|Authors||Y. Lu, X. Huang, K. Zhang, S. Maharjan and Y. Zhang|
|Title||Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles|
|Project(s)||Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Journal||IEEE Transactions on Vehicular Technology|
|Pagination||4298 - 4311|
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.