VahanSWARM: A Blockchain-Driven Swarm Learning Framework for Secure Intelligent Vehicle Supply Chain Networks
Abstract
Abstract The global Automotive ecosystem is experiencing rapid expansion with Global light vehicle production expected to reach 92.6 million units in 2026, while connected vehicles are projected to grow to 2.1 billion by 2035. To accommodate the rapidly growing number of vehicles and maintain operational readiness, intelligent vehicle supply chains must become future-ready through accurate prediction of near-future demand. The increasing adoption of intelligent analytics in intelligent vehicle supply chains has introduced significant challenges related to data privacy preservation, centralized dependency, fragmented data silos, and secure demand prediction. To address these limitations, this paper proposes VahanSWARM, a blockchain-assisted swarm learning framework for secure and intelligent vehicle supply chain networks. The proposed framework enables collaborative demand forecasting without sharing raw datasets among participating entities using decentralized swarm learning with blockchain-based integrity verification. Automotive stakeholders, namely intelligent vehicles, manufacturers, dealers, and customers, locally train predictive models and exchange only prediction-level information during swarm synchronization. The Swarm learning framework enables fully decentralized peer-to-peer collaborative learning without introducing a single point of failure. A blockchain logging mechanism that uses SHA-256 hashing is incorporated to ensure auditability, traceability, and tamper…
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