Open Access

ARTICLE

EdgeGuard-IoT: 6G-Enabled Edge Intelligence for Secure Federated Learning and Adaptive Anomaly Detection in Industry 5.0

Mohammed Naif Alatawi^*

Information Technology Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia

* Corresponding Author: Mohammed Naif Alatawi. Email:

(This article belongs to the Special Issue: Collaborative Edge Intelligence and Its Emerging Applications)

Computers, Materials & Continua 2025, 85(1), 695-727. https://doi.org/10.32604/cmc.2025.066606

Received 12 April 2025; Accepted 26 May 2025; Issue published 29 August 2025

Abstract

Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0. Latency, privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems. We demonstrate that, to overcome these challenges, for instance, the EdgeGuard-IoT framework, a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid, is needed on the edge to integrate Secure Federated Learning (SFL) and Adaptive Anomaly Detection (AAD). With ultra-reliable low latency communication (URLLC) of 6G, artificial intelligence-based network orchestration, and massive machine type communication (mMTC), EdgeGuard-IoT brings real-time, distributed intelligence on the edge, and mitigates risks in data transmission and enhances privacy. EdgeGuard-IoT, with a hierarchical federated learning framework, helps edge devices to collaboratively train models without revealing the sensitive grid data, which is crucial in the smart grid where real-time power anomaly detection and the decentralization of the energy management are a big deal. The hybrid AI models driven adaptive anomaly detection mechanism immediately raises the thumb if the grid stability and strength are negatively affected due to cyber threats, faults, and energy distribution, thereby keeping the grid stable with resilience. The proposed framework also adopts various security means within the blockchain and zero-trust authentication techniques to reduce the adversarial attack risks and model poisoning during federated learning. EdgeGuard-IoT shows superior detection accuracy, response time, and scalability performance at a much reduced communication overhead via extensive simulations and deployment in real-world case studies in smart grids. This research pioneers a 6G-driven federated intelligence model designed for secure, self-optimizing, and resilient Industry 5.0 ecosystems, paving the way for next-generation autonomous smart grids and industrial cyber-physical systems.

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