Privacy-Enhancing Federated Learning Models for Cybersecurity in IoT Networks
Article Sidebar
Page Numbers:
286-301
Digital Object Identifier:
10.58578/ijemt.v4i2.9653
Viewed: 0 times
Downloaded: 0 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:Mohammed Ajuji1, Yusuf Musa Malgwi2, Asabe Sandra Ahmadu3, Mustapha Ismail4, Abubakar Muhammad Hammari5*
Copyright :Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity risks by exposing distributed connected devices to increasingly complex and pervasive threats. Conventional centralized security mechanisms often struggle to accommodate the heterogeneous and decentralized structure of IoT networks. This study investigates Federated Learning (FL) as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead. A novel FL-based Intrusion Detection System (IDS) architecture was developed using Convolutional Neural Networks (CNNs) for anomaly detection and the Federated Averaging (FedAvg) algorithm for aggregating local model updates. The framework was evaluated on standard IoT datasets under non-independent and identically distributed (non-IID) data conditions to simulate heterogeneous real-world environments. Experimental results demonstrate that the proposed system achieved a detection accuracy of 94.6%, an F1-score of 93.8%, and a recall of 92.7%, outperforming centralized and standalone local learning methods. The framework also reduced communication overhead by 35% and achieved convergence 28% faster than conventional approaches. These findings demonstrate that FL can provide a scalable, privacy-preserving, and computationally efficient foundation for strengthening IoT cybersecurity. This study contributes a decentralized machine-learning architecture for real-time, adaptive, and privacy-conscious intrusion detection in large-scale IoT environments.
Citation Metrics:
Downloads
Citation Metrics & Similar Scopus Articles
-
Naemi Z. (2027)The Relationship between Second Language Learning Strategies, Learning Engagement, and Writing Skill in the Arabic Writing CurriculumLanguage Related Research, 17(4), 331-360
-
Wu Y. (2027)Strategies of Designing High-Efficiency Electrolyte Additives for Aqueous Magnesium Batteries: A ReviewNano Micro Letters, 19(1)
-
Fazeli F. (2027)The representation of the properties of the social actors in the two poetries of“Aghadar”and “Fokhos”written by Sheevan Foumani in accordance with the socio-semantic model and semio-semantic analysisLanguage Related Research, 17(4), 397-431
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Alsamiri, J., & Alsubhi, K. (2023). Federated learning for intrusion detection systems in Internet of Vehicles: A general taxonomy, applications, and future directions. Future Internet, 15(12), Article 403. https://doi.org/10.3390/fi15120403
Yaacoub, J.-P. A., Noura, H. N., & Salman, O. (2023). Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions. Internet of Things and Cyber-Physical Systems, 3, 155–179. https://doi.org/10.1016/j.iotcps.2023.04.001
Belarbi, O., Spyridopoulos, T., Anthi, E., Mavromatis, I., Carnelli, P., & Khan, A. (2023). Federated deep learning for intrusion detection in IoT networks. In GLOBECOM 2023—2023 IEEE Global Communications Conference (pp. 237–242). IEEE. https://doi.org/10.1109/GLOBECOM54140.2023.10437860
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., Van Overveldt, T., Petrou, D., Ramage, D., & Roselander, J. (2019). Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems, 1, 374–388. https://proceedings.mlsys.org/paper_files/paper/2019/hash/7b770da633baf74895be22a8807f1a8f-Abstract.html
Chaurasia, N., Ram, M., Verma, P., Mehta, N., & Bharot, N. (2024). A federated learning approach to network intrusion detection using residual networks in industrial IoT networks. The Journal of Supercomputing, 80(13), 18325–18346. https://doi.org/10.1007/s11227-024-06153-2
Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761–768. https://doi.org/10.1016/j.future.2017.08.043
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2019). Federated learning for mobile keyboard prediction [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1811.03604
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022). A survey on federated learning for resource-constrained IoT devices. IEEE Internet of Things Journal, 9(1), 1–24. https://doi.org/10.1109/JIOT.2021.3095077
Javeed, D., Saeed, M. S., Adil, M., Kumar, P., & Jolfaei, A. (2024). A federated learning-based zero trust intrusion detection system for Internet of Things. Ad Hoc Networks, 162, Article 103540. https://doi.org/10.1016/j.adhoc.2024.103540
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., … Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083
Kaur, I., & Jadhav, A. J. (2023). Federated learning in IoT: A survey from a resource-constrained perspective [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2308.13157
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020). Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics, 16(6), 4177–4186. https://doi.org/10.1109/TII.2019.2942190
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. In A. Singh & J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 54, pp. 1273–1282). PMLR. https://proceedings.mlr.press/v54/mcmahan17a.html
Moustafa, N., Creech, G., Slay, J., & Turnbull, B. (2021). Anomaly detection in IoT network traffic using unsupervised deep learning. Computer Networks, 164, Article 106924.
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2022). Privacy-preserved federated learning for healthcare applications: A comprehensive survey. IEEE Transactions on Neural Networks and Learning Systems, 33(3), 1157–1178.
Nguyen, V. T., & Beuran, R. (2025). FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection. Computers & Security, 151, Article 104337. https://doi.org/10.1016/j.cose.2025.104337
Shen, J., Yang, W., Chu, Z., Fan, J., Niyato, D., & Lam, K.-Y. (2024). Effective intrusion detection in heterogeneous Internet-of-Things networks via ensemble knowledge distillation-based federated learning. In ICC 2024—IEEE International Conference on Communications (pp. 2034–2039). IEEE. https://doi.org/10.1109/ICC51166.2024.10622262
Sun, S., Sharma, P., Nwodo, K., Stavrou, A., & Wang, H. (2025). FedMADE: Robust federated learning for intrusion detection in IoT networks using a dynamic aggregation method. In N. Mouha & N. Nikiforakis (Eds.), Information security: ISC 2024 (Lecture Notes in Computer Science, Vol. 15258, pp. 286–306). Springer. https://doi.org/10.1007/978-3-031-75764-8_15
Truex, S., Liu, L., Gursoy, M. E., Yu, L., & Wei, W. (2019). Demystifying membership inference attacks in machine learning as a service. IEEE Transactions on Services Computing, 14(6), 2073–2089. https://doi.org/10.1109/TSC.2019.2897556
Gosselin, R., Vieu, L., Loukil, F., & Benoit, A. (2022). Privacy and security in federated learning: A survey. Applied Sciences, 12(19), Article 9901. https://doi.org/10.3390/app12199901
Ajuji, M., Dawaki, M., Mohammed, A., & Ahmad, A. (2025). Estimating residential natural gas demand and consumption: A hybrid ensemble machine learning approach. Vokasi Unesa Bulletin of Engineering, Technology and Applied Science, 2(3), 549–557. https://doi.org/10.26740/vubeta.v2i3.40135
Handayani, R. D., Jamal, Z., Alkahfiansyah, M., Rosmalia, L., Sudibyo, N. H., & Herwanto, R. (2025). Intelligent and secure package receiver system utilizing Internet of Things (IoT) technology. Vokasi Unesa Bulletin of Engineering, Technology and Applied Science, 2(3), 581–592. https://doi.org/10.26740/vubeta.v2i3.38254
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. V. (2021). Federated learning for Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1622–1658. https://doi.org/10.1109/COMST.2021.3075439
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020). Towards fair and privacy-preserving federated deep models. IEEE Transactions on Parallel and Distributed Systems, 31(11), 2524–2541. https://doi.org/10.1109/TPDS.2020.2996273
Xie, T., Liu, J., Zhang, C., & Chen, M. (2023). Adaptive federated learning for heterogeneous IoT environments: A survey. IEEE Internet of Things Journal, 10(5), 3212–3228. https://doi.org/10.1109/JIOT.2022.3194719
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024). Federated learning in smart healthcare: A comprehensive review on privacy, security, and predictive analytics with IoT integration. Healthcare, 12(24), Article 2587. https://doi.org/10.3390/healthcare12242587
Alatawi, M. N. (2025). SAFEL-IoT: Secure adaptive federated learning with explainability for anomaly detection in 6G-enabled smart industry 5.0. Electronics, 14(11), Article 2153. https://doi.org/10.3390/electronics14112153
Alazab, A., Khraisat, A., Singh, S., & Jan, T. (2023). Enhancing privacy-preserving intrusion detection through federated learning. Electronics, 12(16), Article 3382. https://doi.org/10.3390/electronics12163382
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095
Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client-level perspective [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1712.07557
Ajuji, M., Hamid, R. M., Emmanuel, D. U., Turaki, A. M., & Muhammad, A. N. (2021). Application of machine learning algorithms for fake news detection: A review. ATBU Journal of Science, Technology and Education, 9(4), 91–100.
Guerra-Manzanares, A., Lechuga Lopez, L. J., Maniatakos, M., & Shamout, F. E. (2023). Privacy-preserving machine learning for healthcare: Open challenges and future perspectives. In H. Chen & L. Luo (Eds.), Trustworthy machine learning for healthcare (Lecture Notes in Computer Science, Vol. 13932, pp. 25–40). Springer. https://doi.org/10.1007/978-3-031-39539-0_3
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., Zhu, X., Wang, J., Shen, L., Zhao, P., Kang, Y., Liu, Y., Raskar, R., Yang, Q., Annavaram, M., & Avestimehr, S. (2020). FedML: A research library and benchmark for federated machine learning [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2007.13518
Karras, A., Giannaros, A., Theodorakopoulos, L., Krimpas, G. A., Kalogeratos, G., Karras, C., & Sioutas, S. (2023). FLIBD: A federated learning-based IoT big data management approach for privacy-preserving over Apache Spark with FATE. Electronics, 12(22), Article 4633. https://doi.org/10.3390/electronics12224633
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in neural information processing systems 30 (pp. 3146–3154). Curran Associates, Inc. https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021). Federated learning for Internet of Things: Recent advances, taxonomy, and open challenges. IEEE Communications Surveys & Tutorials, 23(3), 1759–1799. https://doi.org/10.1109/COMST.2021.3090430
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019). Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Generation Computer Systems, 100, 779–796. https://doi.org/10.1016/j.future.2019.05.041
Riley, G. F., & Henderson, T. R. (2010). The ns-3 network simulator. In K. Wehrle, M. Günes, & J. Gross (Eds.), Modeling and tools for network simulation (pp. 15–34). Springer. https://doi.org/10.1007/978-3-642-12331-3_2
Ruzafa-Alcázar, P., Fernández-Saura, P., Mármol-Campos, E., González-Vidal, A., Hernández-Ramos, J. L., Bernal-Bernabe, J., & Skarmeta, A. (2022). Intrusion detection based on privacy-preserving federated IDS using differential privacy. IEEE Access, 10, 62098–62113. https://doi.org/10.1109/ACCESS.2022.3178054
Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018). A generic framework for privacy preserving deep learning [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1811.04017
Sarhan, M., Lo, W. W., Layeghy, S., & Portmann, M. (2022). HBFL: A hierarchical blockchain-based federated learning framework for a collaborative IoT intrusion detection. Computers & Electrical Engineering, 103, Article 108379. https://doi.org/10.1016/j.compeleceng.2022.108379
Abubakar, A., Ajuji, M., & Turaki, A. M. (2023). Diagnostic accuracy of deep learning in medical image analysis: A case study using deep burns [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-2792487/v1