Leveraging Federated Learning and Edge Computing for Privacy-Preserving Real-Time Anomaly Detection in IoT Networks
Keywords:
IoT, Networks, ComputingAbstract
The rapid proliferation of Internet of Things (IoT) networks has heightened the need for robust, privacy-preserving security mechanisms that ensure real-time anomaly detection. This article explores the integration of federated learning (FL) and edge computing as a promising approach to address challenges related to privacy, latency, and resource constraints in IoT environments. Employing a qualitative research methodology, this study analyzes existing literature and emerging frameworks to comprehensively assess the advantages, challenges, and future research directions of applying FL and edge computing for anomaly detection in IoT. Findings highlight that FL combined with lightweight anomaly detection algorithms deployed at the edge can significantly enhance privacy while ensuring timely intrusion detection, despite heterogeneity and limited device resources. The study suggests pathways for developing adaptive, scalable, and secure IoT networks leveraging these paradigms.
Downloads
References
Alasbali, N., Ahmad, J., Siddique, A. A., Saidani, O., Al Mazroa, A., Raza, A., Ullah, R., & Khan, M. S. (2025). Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing. Frontiers in Computer Science, 7. https://doi.org/10.3389/fcomp.2025.1550677
Asiri, F., Malwi, W. Al, Masood, F., Alshehri, M. S., Zhukabayeva, T., Shah, S. A., & Ahmad, J. (2025). Privacy Preserving Federated Anomaly Detection in IoT Edge Computing Using Bayesian Game Reinforcement Learning. Computers, Materials & Continua, 84(2), 3943–3960. https://doi.org/10.32604/cmc.2025.066498
Bae, W. D., Alkobaisi, S., Horak, M., Bankar, S., Bhuvaji, S., Kim, S., & Park, C.-S. (2025). Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications. IEEE Access, 13, 16584–16602. https://doi.org/10.1109/ACCESS.2025.3532222
Eman Shalabi. (2025). A Survey of Federated Learning Privacy Preservation Techniques for Malicious Behavior Detection. Journal of Information Systems Engineering and Management, 10(51s), 659–667. https://doi.org/10.52783/jisem.v10i51s.10438
Ibrahim, A., & Gebali, F. (2024). Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution. Applied Sciences, 14(10), 4085. https://doi.org/10.3390/app14104085
Kais, S., Bhatia, A., & Alam, M. (2023). Quantum federated learning in healthcare: The shift from development to deployment and from models to data. https://doi.org/10.21203/rs.3.rs-2723753/v1
Mahesh Kolli. (2025). IoT-Enabled Cybersecurity for Datacenters - Real-Time Threat Monitoring and Incident Response. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 739–746. https://doi.org/10.32628/CSEIT25112412
Mughal, F. R., He, J., Das, B., Dharejo, F. A., Zhu, N., Khan, S. B., & Alzahrani, S. (2024). Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering. Scientific Reports, 14(1), 28746. https://doi.org/10.1038/s41598-024-78239-z
Odeh, J. O., Yang, X., Samuel, O. W., Dhelim, S., & Nwakanma, C. I. (2025). Systematic investigation of privacy preservation techniques for industrial IoT-enabled critical edge network Infrastructure. Cluster Computing, 28(6), 407. https://doi.org/10.1007/s10586-025-05114-5
S, M., & K R, J. (2025). Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management. Scientific Reports, 15(1), 27524. https://doi.org/10.1038/s41598-025-12225-x
Tawfik, A. M., Al-Ahwal, A., Eldien, A. S. T., & Zayed, H. H. (2025). PriCollabAnalysis: privacy-preserving healthcare collaborative analysis on blockchain using homomorphic encryption and secure multiparty computation. Cluster Computing, 28(3), 191. https://doi.org/10.1007/s10586-024-04928-z
Tiwari, P. S., Pali, P. P., Patel, P. A., Pasi, J., & Jain, M. (2023). Optimizing Data Processing and Security in Edge Computing For Iot Applications. International Journal of Innovative Research in Science,Engineering and Technology, 12(05), 8040–8043. https://doi.org/10.15680/IJIRSET.2023.1205494
Yan, H., Lin, X., Li, S., Peng, H., & Zhang, B. (2025). Global or Local Adaptation? Client-Sampled Federated Meta-Learning for Personalized IoT Intrusion Detection. IEEE Transactions on Information Forensics and Security, 20, 279–293. https://doi.org/10.1109/TIFS.2024.3516548
Yan, Q., Wang, G., Zhu, D., & Li, J. (2025). FedIoT: Optimizing the Communication-Efficient Federated Learning Aggregation Algorithm Under Heterogeneous Data for Large-Scale IoT. International Journal of Pattern Recognition and Artificial Intelligence, 39(11). https://doi.org/10.1142/S0218001425520135
Zhang, Y., Behnia, R., Yavuz, A. A., Ebrahimi, R., & Bertino, E. (2025). Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security. IEEE Transactions on Dependable and Secure Computing, 1–17. https://doi.org/10.1109/TDSC.2025.3568704
