Risk-Aware Adaptive Federated Learning for Cyber-Secure Edge-AI in Smart Edge-IoT Environments

Tanveer Ahmad^1,*, Tahani Alsubait², Amina Salhi³, Amani Ibraheem⁴, Muhammad Asim Saleem⁵
1 Department of Computer Science and Engineering, University of Cyprus, Nicosia, Cyprus
2 Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
4 College of Computer Science, King Khalid University, Abha, Saudi Arabia
5 Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
* Corresponding Author: Tanveer 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.2026.080285

Received 06 February 2026; Accepted 15 April 2026; Published online 18 May 2026

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Abstract

The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning, data heterogeneity, and adversarial manipulation. This paper proposes a new risk-aware adaptive learning model that federated Edge-AI systems explicitly simulates cyber risk in the process of local training and global aggregation. The proposed solution combines stochastic optimization and adversarial risk bounding with adaptive gradient correction to develop strong learning in non-IID data distributions and malicious client behavior. Convergence guarantees are defined by the theoretical analysis in the case of limited adversarial perturbations. The proposed framework achieves up to 95% detection accuracy and demonstrates more than 20% improvement in robustness, where robustness is defined as the relative degradation in detection performance under adversarial perturbations. The performance is evaluated against state-of-the-art baselines, including HADA-FL and centralized training on the Edge-IIoTset dataset, with results reported as averages over multiple randomized runs. Furthermore, the model converges within 50 communication rounds, which corresponds to a fixed training horizon rather than an early-stopping criterion. These findings demonstrate the usefulness of risk-sensitive adaptive learning in safe and trustworthy Edge-AI implementation in a new generation edge-IoT environment.