Preprint
Blockchain-Enabled Federated Learning Approach for Vehicular Networks
SSRN Electronic Journal
Elsevier
5th International Conference on Sustainable Technologies for Industry 5.0 (STI 2023) (Dhaka, Bangladesh, 09/12/2023–10/12/2023)
2023
Abstract
Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system security. The current centralized data-sharing paradigm in these systems raises particular concerns about data privacy. Recognizing these challenges, the shift towards decentralized interactions in technology, as echoed by the principles of Industry 5.0, becomes paramount. This work is closely aligned with these principles, emphasizing decentralized, human-centric, and secure technological interactions in an interconnected vehicular ecosystem. To embody this, we propose a practical approach that merges two emerging technologies: Federated Learning (FL) and Blockchain. The integration of these technologies enables the creation of a decentralized vehicular network. In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability. Initial experiments show that compared to conventional decentralized federated learning techniques, our proposed approach significantly enhances the performance and security of vehicular networks. The system’s accuracy stands at 91.92%. While this may appear to be low in comparison to state-of-the-art federated learning models, our work is noteworthy because, unlike others, it was achieved in a malicious vehicle setting. Despite the challenging environment, our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks
Details
- Title
- Blockchain-Enabled Federated Learning Approach for Vehicular Networks
- Authors/Creators
- Shirin Sultana - Bangladesh University of Business and TechnologyJahin HossainMaruf Billah - Bangladesh University of Business and TechnologyHasibul Hossain Shajeeb - Bangladesh University of Business and TechnologySaifur Rahman - Bangladesh University of Business and TechnologyKeyvan Ansari - Murdoch UniversityKhondokar Fida Hasan - RMIT University
- Publication Details
- SSRN Electronic Journal
- Conference
- 5th International Conference on Sustainable Technologies for Industry 5.0 (STI 2023) (Dhaka, Bangladesh, 09/12/2023–10/12/2023)
- Publisher
- Elsevier
- Identifiers
- 991005626056707891
- Copyright
- © 2023 Elsevier Inc.
- Murdoch Affiliation
- College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Preprint
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