Work

Blockchain-enabled Privacy-preserving Federated Learning With Deep Capsule Networks for Energy-efficient Speed Control in Autonomous Vehicles

Year: 2026

Type: article

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.

Source: Communications - Scientific letters of the University of Zilina

DOI: 10.26552/com.c.2026.021

Authors: N Sanjith, M. Nithya, Sreeju P Sreedharan, N Sanjith, M. Nithya, Sreeju P Sreedharan

Institution: Vinayaka Missions University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W7154170771