Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
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...
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| Published in | IEEE transactions on vehicular technology Vol. 69; no. 4; pp. 4298 - 4311 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| 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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| AbstractList | 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. |
| Author | Zhang, Ke Lu, Yunlong Zhang, Yan Maharjan, Sabita Huang, Xiaohong |
| Author_xml | – sequence: 1 givenname: Yunlong orcidid: 0000-0001-9552-5130 surname: Lu fullname: Lu, Yunlong email: yunlong.lu@ieee.org organization: Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 2 givenname: Xiaohong orcidid: 0000-0002-7275-2274 surname: Huang fullname: Huang, Xiaohong email: huangxh@bupt.edu.cn organization: Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 3 givenname: Ke orcidid: 0000-0003-3081-7751 surname: Zhang fullname: Zhang, Ke email: zhangke@uestc.edu.cn organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Sabita orcidid: 0000-0002-4616-8488 surname: Maharjan fullname: Maharjan, Sabita email: sabita@simula.no organization: Simula Metropolitan Center for Digital Engineeing, Oslo, Norway – sequence: 5 givenname: Yan orcidid: 0000-0002-8561-5092 surname: Zhang fullname: Zhang, Yan email: yanzhang@ieee.org organization: University of Oslo, Norway |
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| SubjectTerms | Architecture Asynchronous federated learning Blockchain Cryptography Data privacy Data retrieval Data sharing Deep reinforcement learning Device-to-device communication Federated learning Information sharing Internet of Vehicles Machine learning Privacy Reliability Resource management Security Wireless networks |
| Title | Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles |
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