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Systematic Review

A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning

by
Jallal-Eddine Moussaoui
*,
Mehdi Kmiti
,
Khalid El Gholami
and
Yassine Maleh
LaSTI Laboratory, ENSA Khouribga, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
*
Author to whom correspondence should be addressed.
J. Cybersecur. Priv. 2025, 5(3), 41; https://doi.org/10.3390/jcp5030041
Submission received: 4 May 2025 / Revised: 13 June 2025 / Accepted: 23 June 2025 / Published: 2 July 2025
(This article belongs to the Topic Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence)
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Abstract

Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated learning (FL), by contrast, enables decentralized model training but suffers from scalability and latency issues. Hybrid AI models, which integrate ML and FL techniques, have emerged as a promising solution to balance performance, privacy, and scalability in cybersecurity. This systematic review investigates the current landscape of hybrid AI models, evaluating their strengths and limitations across five key dimensions: accuracy, privacy preservation, scalability, explainability, and robustness. Findings indicate that hybrid models consistently outperform standalone approaches, yet challenges remain in real-time deployment and interpretability. Future research should focus on improving explainability, optimizing communication protocols, and integrating secure technologies such as blockchain to enhance real-world applicability.
Keywords: cybersecurity; federated machine learning; machine learning; federated learning; deep learning; hybrid AI models; accuracy; privacy; explainability; scalability; robustness cybersecurity; federated machine learning; machine learning; federated learning; deep learning; hybrid AI models; accuracy; privacy; explainability; scalability; robustness

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MDPI and ACS Style

Moussaoui, J.-E.; Kmiti, M.; El Gholami, K.; Maleh, Y. A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning. J. Cybersecur. Priv. 2025, 5, 41. https://doi.org/10.3390/jcp5030041

AMA Style

Moussaoui J-E, Kmiti M, El Gholami K, Maleh Y. A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning. Journal of Cybersecurity and Privacy. 2025; 5(3):41. https://doi.org/10.3390/jcp5030041

Chicago/Turabian Style

Moussaoui, Jallal-Eddine, Mehdi Kmiti, Khalid El Gholami, and Yassine Maleh. 2025. "A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning" Journal of Cybersecurity and Privacy 5, no. 3: 41. https://doi.org/10.3390/jcp5030041

APA Style

Moussaoui, J.-E., Kmiti, M., El Gholami, K., & Maleh, Y. (2025). A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning. Journal of Cybersecurity and Privacy, 5(3), 41. https://doi.org/10.3390/jcp5030041

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