Blockchain-Enabled Federated Learning Approach for Vehicular Networks
- URL: http://arxiv.org/abs/2311.06372v1
- Date: Fri, 10 Nov 2023 19:51:18 GMT
- Title: Blockchain-Enabled Federated Learning Approach for Vehicular Networks
- Authors: Shirin Sultana, Jahin Hossain, Maruf Billah, Hasibul Hossain Shajeeb,
Saifur Rahman, Keyvan Ansari, Khondokar Fida Hasan
- Abstract summary: We propose a practical approach that merges two emerging technologies: Federated Learning (FL) and the vehicular ecosystem.
In this setting, vehicles can learn from each other without compromising privacy while also ensuring data integrity and accountability.
Our method maintains high accuracy, making it a competent solution for preserving data privacy in vehicular networks.
- Score: 3.1749005168397617
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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