The intensive development of smart city cyber-infrastructure has aggravated the security issues because of the heterogeneity of devices, the volume of constantly changing data, and the lack of confidentiality in the long term. Traditional public-key cryptographic systems like RSA and ECC are becoming susceptible to quantum-powered attacks and are inappropriate to use in smart city applications in the future. Despite their quantum resistance, post-quantum cryptographic (PQC) schemes are not widely used in practice due to high computational cost, difficulty in key management, and inflexibility to dynamic cyber-threats. Furthermore, the current security systems do not tend to be decentralized, but the sources of intelligence are usually centralized which creates issues of privacy and scalability. This paper mitigates these shortages by suggesting a safe post-quantum architecture that incorporates an adjustable federated learning architecture and a hybridized lattice-based Key Encapsulation Mechanism to smart city cyber-infrastructure. The KEM based on lattice provides resistant to quantum key exchange, whereas federated learning provides decentralized threat intelligence without sensitive local information disclosure. An adaptive aggregation approach dynamically scales model updates based on node reliability and threat intensity, improving resilience to poisoning and inference attacks. Benchmark post-quantum parameters and public intrusion datasets, such as UNSW-NB15 and CICIDS2017 are tested in the framework in a simulated smart city setting. The experimental findings indicate a maximum common exchange latency reduction of 21 percent, versus standalone lattice schemes, and a threat detection rate of 97.8 percent with stable convergence. The results have shown the efficiency of integrating post-quantum cryptography with adaptive federated intelligence in ensuring the security of the next generation smart city infrastructures.
The intensive development of smart city cyber-infrastructure has aggravated the security issues because of the heterogeneity of devices, the volume of constantly changing data, and the lack of confidentiality in the long term. Traditional public-key cryptographic systems like RSA and ECC are becoming susceptible to quantum-powered attacks and are inappropriate to use in smart city applications in the future. Despite their quantum resistance, post-quantum cryptographic (PQC) schemes are not widely used in practice due to high computational cost, difficulty in key management, and inflexibility to dynamic cyber-threats. Furthermore, the current security systems do not tend to be decentralized, but the sources of intelligence are usually centralized which creates issues of privacy and scalability. This paper mitigates these shortages by suggesting a safe post-quantum architecture that incorporates an adjustable federated learning architecture and a hybridized lattice-based Key Encapsulation Mechanism to smart city cyber-infrastructure. The KEM based on lattice provides resistant to quantum key exchange, whereas federated learning provides decentralized threat intelligence without sensitive local information disclosure. An adaptive aggregation approach dynamically scales model updates based on node reliability and threat intensity, improving resilience to poisoning and inference attacks. Benchmark post-quantum parameters and public intrusion datasets, such as UNSW-NB15 and CICIDS2017 are tested in the framework in a simulated smart city setting. The experimental findings indicate a maximum common exchange latency reduction of 21 percent, versus standalone lattice schemes, and a threat detection rate of 97.8 percent with stable convergence. The results have shown the efficiency of integrating post-quantum cryptography with adaptive federated intelligence in ensuring the security of the next generation smart city infrastructures.
The research investigates the implications of quantum computing on smart city infrastructure with a specific focus on addressing cybersecurity challenges. Smart cities, reliant on interconnected systems, are increasingly vulnerable to zero-day attacks exacerbated by quantum computing advancements. This study aims to develop a robust defense framework integrating quantum-resilient cryptographic techniques, artificial intelligence-based anomaly detection systems, and hybrid quantum-classical simulations. The methodology utilizes lattice-based encryption schemes and hash-based signatures to fortify communications, while machine learning models such as Long Short-Term Memory networks and Convolutional Neural Networks identify complex patterns indicative of cyber threats. Evaluation within a simulated smart city environment demonstrates high detection accuracy of 97.8 percent, reduced false positive rates, and efficient resource consumption, validating the framework’s practical applicability. By bridging theoretical advancements and practical implementation, this work enhances the resilience of urban infrastructures against quantum-augmented threats. Findings contribute to the growing body of knowledge by offering scalable and adaptable solutions tailored to the resource-constrained nature of smart cities. Future research can extend this work by addressing energy efficiency, interoperability across sectors, and incorporating federated learning paradigms to enhance distributed anomaly detection. These advancements hold significant implications for securing next-generation urban systems while ensuring sustainability and operational efficiency.
ABSTRACT The increasing complexity of smart grid IoT ecosystems demands security architectures capable of resisting quantum‐era threats, protecting data privacy, and scaling across large distributed infrastructures. This study introduces a novel hybrid security framework that integrates quantum cryptography, deep learning–based intrusion detection, and federated learning into a unified, high‐assurance design tailored for next‐generation smart grid environments. The architecture employs quantum key distribution (QKD) for secure key generation, an adaptive deep feature obfuscation layer to mitigate adversarial manipulation, and a privacy‐preserving federated learning pipeline that eliminates centralized data exposure. Simulation‐based evaluations demonstrate substantial performance gains, achieving a low quantum bit error rate (1.8%), high key‐generation throughput (4900 keys/s), low latency (18 ms), and intrusion detection accuracy of 98.7%, consistently outperforming conventional cryptographic and machine learning baselines. The framework further exhibits enhanced resilience against quantum‐based and adversarial attacks, with efficient performance maintained even under increasing network density. While real‐world deployment will require hardware‐in‐the‐loop validation and optimization for heterogeneous traffic conditions, the results indicate strong potential for securing future smart grid IoT infrastructures and supporting sustainable smart city applications.
Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.
The rapid expansion of IoT ecosystems has increased concerns regarding data privacy, security, and model integrity, particularly in environments vulnerable to sophisticated adversarial and quantum-enabled attacks. Traditional cryptographic and Federated Learning (FL) methods cannot keep up with the demands for confidentiality and robustness in vast, diverse networks. Therefore, this study presents a Quantum-Inspired Cryptographic Framework that uses Quantum Neural Networks, Post-Quantum Cryptography, and secure Federated Learning to enhance intrusion detection systems and provide quantum resilient communication. The methodology uses the UNSW-NB15 dataset, which has been cleaned, encoded, and normalized with reduced features balanced by SMOTE before splitting into 70 percent training and 30 percent testing. A QNN-based FL model can then be trained while Kyber-512 and NTRUEncrypt secure all updates to the model, as well as node communications, against both classical and quantum threats. The experimental results show that the proposed model significantly outperforms classical FL frameworks, achieving an accuracy of 98.1%, precision of 97.5%, recall of 97.9%, and F1-score of 97.7%, even under very challenging adversarial conditions. QICF is therefore a robust, privacy-preserving, and attack-resilient solution for next-generation large-scale IoT networks.
Federated Learning (FL) enables collaborative machine learning by aggregating local models from decentralized clients without sharing raw data. However, the parameter exchange process in standard FL architectures is vulnerable to emerging quantum computing threats. Specifically, Shor's algorithm, executable on large-scale quantum computers, can break conventional asymmetric cryptography such as RSA and ECDH in polynomial time, thereby threatening the security of FL systems that rely on these traditional public-key infrastructures for parameter exchange. This study addresses this vulnerability by implementing the recently standardized post-quantum cryptographic protocol, ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism), within a FedAvg-based FL architecture. The integration is designed to secure the parameter exchange pipeline without compromising the neural network's performance. Experimental results on a simulated environment utilizing the MNIST dataset demonstrate that the ML-KEM integration preserves 100% of the global model accuracy. Furthermore, the cryptographic latency overhead introduced by encapsulation and decapsulation remains highly efficient, proving its potential as a robust security layer. While current evaluations focus on small-scale deployments, the proposed architecture establishes a foundational post-quantum security framework for future privacy-preserving distributed learning systems.
Industrial innovations, driven by technologies such as the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), have revolutionized various operational domains, including supply chain management, equipment monitoring, predictive maintenance, and quality control. Deep Learning (DL) has become instrumental in addressing complex, data-driven challenges within industrial settings, relying on large datasets collected from heterogeneous sources. However, centralizing these datasets on cloud platforms poses significant privacy and security risks. Federated Learning (FL) offers a promising solution by enabling distributed model training across multiple nodes while preventing the exchange of raw data. Despite its advantages, FL introduces new vulnerabilities, especially related to the security of communication channels between participating entities. Traditional cryptographic mechanisms, such as digital signatures and model encryption, can mitigate these risks, but the emergence of quantum computing threatens the robustness of conventional solutions. This work explores the integration of Post-Quantum Cryptography (PQC) into FL to enhance security without incurring significant performance degradation. A modular FL architecture with three security levels is proposed: (i) an unprotected baseline; (ii) authenticated communication using ML-DSA digital signatures; and (iii) full protection combining ML-DSA and ML-KEM-512-based key encapsulation. Implemented within the Flower framework, the architecture is evaluated under simulated MitM attacks. Experimental results demonstrate that PQC-enhanced schemes effectively mitigate quantum-resistant threats while maintaining acceptable computational overhead, thereby ensuring model integrity and data confidentiality.