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.
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.
The IoT Smart grid systems require privacy-sensitive, scalable, and secure communication, but the traditional cryptographic methods are susceptible to quantum attacks, central failure, and high computational costs. In this paper, the author presents a multi-layered structure that uses various yet complementary approaches in order to solve these issues. Quantum Key Distribution (QKD) is applied to create quantum-resilient key management to provide the secure transmission against future quantum attacks. Homomorphic Encryption (HE) allows the computations to be performed on encrypted data to preserve privacy and minimize the risks of exposure at some point during the processing. Federated Learning (FL) is used to assist the decentralized intelligence, where anomaly detection models are trained together without the need of transferring sensitive raw data. The combination of the two techniques produces a hybrid architecture that is both highly secure and scalable. Experimental validation on real-world smart grid data indicates the 42% communication overhead reduction versus traditional encryption, a 35% improvement in anomaly detection with FL and strong resistance to quantum attack and side-channel attack simulation. The findings support the fact the suggested solution provides end-to-end confidentiality, integrity, and performance efficiency, which will form a basis of quantum-resilience and privacy-preserving communication in future smart grid IoT network.
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.
The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. By integrating parameterized quantum circuits (PQCs) at the local agent level with secure federated learning protocols, the framework enhances detection accuracy while preserving data privacy. A trimmed-mean aggregation scheme and differential privacy mechanisms are embedded to defend against Byzantine behaviors and data-poisoning attacks. The problem is formally modeled as a constrained optimization task, accounting for quantum circuit depth, communication latency, and adversarial resilience. Experimental validation on synthetic smart grid datasets demonstrates that FQML achieves high detection accuracy (≥96.3%), maintains robustness under adversarial perturbations, and reduces communication overhead by 28.6% compared to classical federated baselines. These results substantiate the viability of quantum-enhanced federated learning as a practical, hardware-conscious approach to distributed cybersecurity in next-generation energy 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 rapid growth of the Internet of Things (IoT) is transforming industries such as healthcare, transportation, and smart infrastructure, but it also amplifies critical concerns regarding data security, privacy, and device authentication. Traditional cryptographic mechanisms offer partial protection, yet they remain vulnerable to emerging quantum computing threats, posing significant risks to IoT systems. Addressing these challenges, this paper introduces FLQC-IoT, a novel security framework that integrates federated learning (FL) with quantum computing (QC) to provide robust, scalable, and future-ready protection. Unlike conventional centralized approaches, FLQC-IoT enables decentralized model training directly on IoT devices, safeguarding sensitive data from exposure. Quantum technologies are incorporated at multiple layers: quantum neural networks accelerate optimization tasks, while quantum key distribution and quantum homomorphic encryption ensure tamper-proof key exchange and secure communication. Furthermore, the framework adapts dynamically to emerging threats through real-time anomaly detection and distributed intelligence, significantly enhancing resilience. Experimental results demonstrate a 15.32 % reduction in computational overhead and a 21.45 % decrease in authentication rounds compared to state-of-the-art methods, highlighting the framework’s efficiency and practical readiness. These findings underscore the significance and transformative impact of FLQC-IoT, providing a scalable, privacy-preserving, and resilient approach to securing IoT networks across critical domains, including healthcare, university campus networks, smart infrastructure, and transportation.
This chapter presents an integrated quantum–federated intelligence framework for secure smart healthcare and smart city infrastructures. The proposed architecture combines edge IoT sensing, quantum-enhanced anomaly detection, federated learning orchestration, and quantum-secure communication layers. Hybrid quantum–classical models improve zero-day detection and high-dimensional pattern separability, while privacy-preserving federated mechanisms ensure data locality and regulatory compliance. Performance metrics, deployment constraints, and NISQ-era challenges are analyzed, outlining a practical roadmap toward scalable and resilient cyber-physical systems.