Privacy-Enhanced Federated Learning Model for Secure Internet of Things Applications: A Systematic Review
DOI:
https://doi.org/10.26740/vubeta.v3i3.46474Keywords:
Edge Intelligence, Cybersecurity, Privacy Enhancing, Internet of Things, Federated LearningAbstract
The rapid growth of Internet of Things (IoT) ecosystems has increased security and privacy risks due to the large volume of sensitive and distributed data generated by connected devices. Traditional machine learning approaches for IoT security rely on centralized data aggregation, which raises privacy concerns, scalability limitations, and regulatory challenges. This study presents a systematic literature review of privacy-enhanced federated learning (PEFL) for secure IoT applications. The review analyzes federated learning architectures, optimization strategies, privacy-preserving mechanisms, and cybersecurity use cases in heterogeneous IoT environments. Findings indicate that federated learning improves privacy by keeping data localized at edge devices, while techniques such as differential privacy and secure aggregation enhance confidentiality. However, these methods introduce trade-offs in accuracy, communication efficiency, and computational overhead. Hierarchical and adaptive FL architectures improve scalability and robustness, particularly for intrusion detection systems. Persistent challenges include non-IID data, device heterogeneity, limited edge resources, and the lack of standardized benchmarks and real-world deployments. PEFL offers a promising framework for securing IoT systems, though further research is needed to address deployment and performance constraints in practical environments.
Author Biographies
Mohammed Ajuji, Department of Computer Science, Faculty of Science, Gombe State University, Gombe State, Nigeria
Mohammed Ajuji is a lecturer in the Department of Computer Science, Gombe State University, Gombe, Nigeria. He received his B.Sc. in Computer Science from Gombe State University in 2012 and his M.Sc. in Computer Science from Abubakar Tafawa Balewa University, Bauchi in 2023. He is currently pursuing a Ph.D. in Computer Science at Modibbo Adama University, Yola, Nigeria. His research interests include artificial intelligence, machine learning, deep learning, and computer vision, with applications in smart systems and sustainable development. He can be contacted at email: [email protected].
Yusuf Musa Malgwi, Department of Computer Science, Faculty of Computing, Modibbo Adama University, Adamawa State, Nigeria
Yusuf Musa Malgwi is a Senior Lecturer and Head of the Department of Computer Science at Modibbo Adama University, Yola, Nigeria. He obtained his B.Tech. in Computer Science from the Federal University of Technology, Yola (now Modibbo Adama University) in 2006 and his M.Sc. in Computer Science from Adamawa State University, Mubi in 2014. He earned his Ph.D. in Computer Science from Modibbo Adama University, Yola in 2019. His research interests include machine learning, artificial intelligence, medical informatics, and computational intelligence, with applications in healthcare systems, cybersecurity, and intelligent decision support. He can be contacted at email: [email protected].
Asabe Sandra Ahmadu, Department of Computer Science, Faculty of Computing, Modibbo Adama University, Adamawa State, Nigeria
Asabe Sandra Ahmadu is a Professor in the Department of Computer Science, Modibbo Adama University, Yola, Nigeria. She obtained her B.Tech. in Computer Science from Federal University of Technology, Yola (now Modibbo Adama University), and her M.Sc. and Ph.D. in Computer Science from Abubakar Tafawa Balewa University, Bauchi. Her research interests include data mining, machine learning, artificial intelligence, and image processing, with applications in education, health informatics, and remote sensing. She can be contacted at email: [email protected].
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