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

By Yunlong Lu, Xiaohong Huang, Kecheng Zhang, Sabita Maharjan, Yan Zhang. IEEE Transactions on Vehicular Technology, 2020

600 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/333736f748c6c5a38528c05ba34ed3d527bf230e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Electrical and Electronic Engineering

In Information Systems

Citations in the corpus

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, methodology): “[106] discuss secure data sharing problem in Internet of Vehicles (IoV), and propose an asynchronous FL scheme based on a hybrid blockchain.”
Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks (2020, background): “have attracted considerable attention and have been well studied for typical MEC applications [13], such as content caching [14] and vehicular data sharing recently [15].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “The research in [52] suggests an asynchronous federated data sharing framework for Internet of Vehicles (IoV) where each vehicle acts as an FL client to”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “Conceptually, the operational process of a classical FL system includes four steps:”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “The authors of [46] propose a novel architecture for the Internet of Vehicles (IoV) that capitalizes on federated learning to alleviate the transmission overhead and address the providers’ privacy concerns.”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, methodology): “This mechanism has been utilized in several studies, such as [219] and [220].”
A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, neutral): “Xie et al. [96] proposed asynchronous federated optimization, [159] discussed on the asynchronous FL for geospatial applications, [160]–[162] proposed a DP-based asynchronous FL strategy for MEC, and [163] presented a blockchain-based secure data sharing strategy for asynchronous FL.”