Citation

Ullah, Fasee and Chowdhury, Md. Tahmid Ashraf and Alhussain, Hitham and Kamal, Shahid and Bhushan, Shashi (2025) Blockchain-Enabled Federated Learning for Collaborative Autonomous Vehicle Navigation. In: 2025 International Conference on Metaverse and Current Trends in Computing, ICMCTC 2025, 10 April 2025 - 11 April 2025, Hybrid, Subang Jaya.

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Abstract

The arrival of Autonomous Vehicles (AVs) is trans-forming contemporary transport systems. However, there are vital challenges, particularly those that involve secure and efficient updates to the navigation model. This paper presents a novel decentralized system that incorporates federated learning and the Polygon Layer 2 blockchain to facilitate collaborative and privacy-preserving navigation model improvements among AVs. Federated learning allows vehicles to collaboratively improve their navigation models without sharing raw sensitive sensor data explicitly, whereas the Polygon blockchain offers transparency, integrity, and scalability of transactions. We also propose blockchain-based incentive frameworks that reward participating vehicles that provide useful model updates, promoting active participation. Theoretical validation of our framework is demonstrated via smart contract deployment on the Polygon network, proving the feasibility of our solution. The suggested system addresses privacy concerns, scalability problems, and transaction costs, thus providing a workable option for the cooperation of decentralized autonomous vehicles.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Autonomous vehicles, blockchain, collaborative navigation, decentralized systems
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	09 Dec 2025 04:37
Last Modified:	09 Dec 2025 04:37
URII:	http://shdl.mmu.edu.my/id/eprint/14986

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