Edge AI Driven Fleet Diagnostics using Distributed Learning across Vehicles
DOI:
https://doi.org/10.48047/tw4zqx21Keywords:
Edge AI, Fleet Diagnostics, Distributed Learning, Federated Learning, Predictive Maintenance, Internet of Things (IoT), Vehicle Health Monitoring, Anomaly Detection, Smart Transportation.Abstract
The rapid growth of connected vehicles and IoT technologies has created new opportunities for intelligent fleet management systems. This paper proposes an Edge AI–driven framework for fleet diagnostics that leverages distributed learning across vehicles to enable real-time monitoring, fault detection, and predictive maintenance. Unlike traditional cloud-centric approaches, the proposed system processes data locally at the vehicle level using edge computing, thereby reducing latency, bandwidth consumption, and privacy risks.
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