Blockchain-enabled privacy-preserving federated learning with deep capsule networks for energy-efficient speed control in autonomous vehicles
Blockchain-enabled privacy-preserving federated learning with deep capsule networks for energy-efficient speed control in autonomous vehicles
Author(s): Sanjith N Sanjith N, M. Nithya, Sreeju P SreedharanSubject(s): Energy and Environmental Studies, Electronic information storage and retrieval, Security and defense, Methodology and research technology, ICT Information and Communications Technologies, Transport / Logistics
Published by: Žilinská univerzita v Žilině
Keywords: autonomous vehicles; energy-efficient speed control; deep capsule networks; federated learning; blockchain;
Summary/Abstract: Rough roads negatively affect the ride quality and energy efficiency of autonomous vehicles (AVs) and demand adaptive speed control. To solve this, this research proposes a Deep Capsule Network integrated with Sparse Kernel K-means Clustering (DCapsNet + SK-K-means) to accurately reconstruct pavement conditions and adapt vehicle speed in real time. The Privacy-Preserving Federated Learning (PPFL) with Hyperledger Fabric blockchain allows federated learning to be trained in a secure and decentralized manner without the transfer of raw sensor data. With the BDD100K dataset, supervised and reinforcement learning are used. According to the obtained results of the experiment, there is a 10.45% increase in ride comfort, 28.23% in energy efficiency, and 98.28% in computational efficiency. The framework attains 93.73% security, 96.46% throughput and 0.83 s operation time at 250 vehicles/km.
Journal: Komunikácie - vedecké listy Žilinskej univerzity v Žiline
- Issue Year: 28/2026
- Issue No: 2
- Page Range: 2062-2072
- Page Count: 10
- Language: English
