Communications - Scientific letters of the University of Zilina X:X | DOI: 10.26552/com.C.2026.021

Blockchain-Enabled Privacy-Preserving Federated Learning with Deep Capsule Networks for Energy-Efficient Speed Control in Autonomous Vehicles

Sanjith N ORCID...1, *, M. Nithya ORCID...1, Sreeju P Sreedharan ORCID...2
1 Vinayaka Mission Research Foundation (DU), Computer Science and Engineering Vinayaka Mission Kirupananda Variyar Engineering College, Centre for Artificial Intelligence and Data Science (CAIDS), Salem, Tamilnadu, India
2 Vinayaka Mission Research Foundation (DU), Vinayaka Mission Kirupananda Variyar Engineering College, Department of Arts and Science, Salem, Tamilnadu, India

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.

Keywords: autonomous vehicles, energy-efficient speed control, deep capsule networks, federated learning, blockchain
Grants and funding:

The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: September 19, 2025; Accepted: January 31, 2026; Prepublished online: April 13, 2026 

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