Researchers led by Gachon University propose an explainable federated learning (XFL) framework that combines on-board training and secure global aggregation with XAI techniques, optimizing electric vehicle energy management and traffic predictions while preserving data privacy in smart urban environments.

Key points

  • Hierarchical federated learning architecture integrates on-vehicle MLP models and secure cloud aggregation to optimize AEV energy consumption and traffic density predictions.
  • SHAP and LIME explainability modules identify critical factors like traffic density, speed, and time-of-day, enhancing transparency in model-driven energy control decisions.
  • Global MLP model reaches R² of 94.73% for energy consumption and 99.83% for traffic density on a 1.2 million–record AEV telemetry dataset.

Why it matters: By uniting federated learning with explainable AI, this approach delivers scalable, real-time energy optimization and transparency, advancing sustainable smart mobility beyond traditional centralized models.

Q&A

  • What is federated learning?
  • How does explainable AI improve model trust?
  • Why choose MLP for federated energy modeling?
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Enhancing smart city sustainability with explainable federated learning for vehicular energy control