Volume 13, Issue 9 (September 2026), Pages: 143-155

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 Review Paper

Towards a heterogeneity-aware federated intelligence framework for proactive cyber-physical defense in the Internet of Energy

 Author(s): 

Laila Nassef *

 Affiliation(s):

Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-9707-1259

 Digital Object Identifier (DOI)

 
https://doi.org/10.21833/ijaas.2026.09.014

 Abstract

The Internet of Energy is a critical national infrastructure that must be protected against security attacks to prevent significant economic losses. Its communication infrastructure integrates electrical power grids with data analytics tools to automate energy generation, transmission, distribution, and consumer-related operations. However, it remains vulnerable to cyber-physical attacks and sensitive data leakage. Recent federated split learning-based intrusion detection systems help preserve data privacy but face significant challenges in large-scale heterogeneous environments. The literature highlights the need to address data, computational, and communication heterogeneity, which can reduce model performance. Therefore, this prospective paper proposes a hierarchical federated split learning framework that distributes processing across local, regional, and global levels while considering cross-layer dependencies. Graph neural networks are integrated to model network topology and provide structured feature embeddings for multi-agent deep reinforcement learning. This integration aims to understand complex infrastructure dependencies, optimize conflicting parameters, coordinate actions, and automate responses. The proposed framework aims to shift cyber-physical defense from centralized, reactive detection towards collaborative, proactive, and predictive systems. It also aims to enhance situational awareness, risk assessment, continuous adaptation, threat mitigation, and response optimization to support the early detection of complex and unknown cyber-physical threats, including coordinated attacks across critical infrastructure, thereby promoting a proactive, cognitive, and sustainable defense ecosystem.

 © 2026 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

Internet of Energy, Federated split learning, Intrusion detection, Cyber-physical security, Proactive defense

 Article history

Received 21 April 2026, Received in revised form 18 September 2026, Accepted 25 September 2026

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Citation:

Nassef L (2026). Towards a heterogeneity-aware federated intelligence framework for proactive cyber-physical defense in the Internet of Energy. International Journal of Advanced and Applied Sciences, 13(9): 143-155

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