Post-Quantum Protected Federated Learning with Explainable and Adaptive Intelligence for Smart City Transportation

Junaid, Malik, Hassan, Algarni, Fahad and Ullah, Insaf (2026) Post-Quantum Protected Federated Learning with Explainable and Adaptive Intelligence for Smart City Transportation. Internet of Things. ISSN 2542-6605 (In Press)

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Abstract

Existing AI-powered Intelligent Transportation Systems (ITS) have limitations in scalability, privacy, and vulnerability to cyberattacks, as well as a lack of transparency in decision-making. In this work, we present a hybrid framework based on Post-Quantum-protected Federated Learning, a lightweight CNN-Transformer model, LIME explanations, and a local model, achieving a loss of 0.02% and a validation accuracy of 98%. At the boundary, congestion is determined using CityFlowV2 traffic camera feeds, which are based on Federated Learning, a distributed training framework that does not require sharing raw data, and the architecture is privacy-respectful. Reinforcement learning trained on OpenStreetMap road networks in Los Angeles coordinates rerouting plans in a simulated environment at the global level, and SHAP provides an explanation of the decision. The Federated aggregation retained accuracy at the zone level, exceeding 97%. Furthermore, this affirms its strength. CRYSTALS-Kyber is used to encrypt V2I and V2V communications, ensuring they are resistant to attacks in the quantum era. The framework is scalable and interpretative, and offers a secure, adaptable, city-neutral blueprint of next-generation ITS.

Item Type:	Article
Uncontrolled Keywords:	post-quantum,federated learning,explainable ai (xai),sdg 11 - sustainable cities and communities ,/dk/atira/pure/sustainabledevelopmentgoals/sustainable_cities_and_communities
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Intelligence and Networks Faculty of Science > Research Groups > Data Science and AI
Depositing User:	LivePure Connector
Date Deposited:	09 Feb 2026 16:37
Last Modified:	09 Feb 2026 16:37
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101879
DOI:	issn:2542-6605

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