FedGNN: Federated Graph Neural Networks for Privacy-Preserving Cyber-Resilient Energy Optimization in IoT-Based Smart Grids

Alanoud Al Mazroa¹, Fahad Masood², Bakri Hussain Awaji³, Mohammad Alhefdi⁴, Abeer Aljohani⁵, Jawad Ahmad^6,*
1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
2 Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
3 Department of Computer Science, Collage of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
4 Computer Engineering Department, King Khalid University, Abha, Saudi Arabia
5 Department of Computer Science and Informatics, Applied College, Taibah University, Madinah, Saudi Arabia
6 Cybersecurity Center, Prince Mohammad Bin Fahd University, Alkhobar, Saudi Arabia
* Corresponding Author: Jawad Ahmad. Email: email
(This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.080134

Received 03 February 2026; Accepted 21 April 2026; Published online 18 May 2026

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Abstract

The rapid integration of Internet of Things (IoT) devices and distributed energy resources into smart grids has improved monitoring, control, and energy efficiency. However, it also exposes the grid to cyberattacks and privacy risks, as increased connectivity and data exchange can significantly disrupt energy management and system stability. Studies focused on centralized cybersecurity mechanisms that lacked scalability and did not emphasize the inherent graph structure of power networks. This study proposes a privacy-preserving and cyber-resilient energy-optimization framework, FedGNN, for IoT-enabled smart grids that jointly integrates federated learning, graph neural network-based trust inference, and trust-aware energy dispatch. The framework dynamically learns node-level trust scores from multi-feature measurements, including load, voltage, frequency, renewable generation, and battery storage, and incorporates them into real-time energy optimization. Results demonstrate that the proposed approach improves system resilience up to 12%, mitigates the impact of compromised nodes, and maintains operational reliability, while preserving the privacy of distributed data. A comparative analysis with baseline methods shows the proposed framework’s superior performance in energy deviation, resilience, and trust-aware decision-making. The results highlight the potential of integrating AI-driven trust mechanisms with federated learning for secure and efficient energy management in future IoT-enabled smart grids.