Federated Quantum Machine Learning for Distributed Cybersecurity in Multi-Agent Energy Systems

Kwabena Addo (), Musasa Kabeya and Evans Eshiemogie Ojo
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Kwabena Addo: Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa
Musasa Kabeya: Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa
Evans Eshiemogie Ojo: Department of Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa

Energies, 2025, vol. 18, issue 20, 1-31

Abstract: 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.

Keywords: adversarial robustness; federated learning; quantum machine learning; smart grid cybersecurity; variational quantum circuits (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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