{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:23:16Z","timestamp":1774495396857,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The rapid expansion of 5G-enabled Internet of Things (IoT) devices in smart homes has heightened the need for robust, privacy-preserving, and real-time cybersecurity mechanisms. Traditional cloud-based security systems often face latency and privacy bottlenecks, making them unsuitable for edge-constrained environments. In this work, we propose Edge-FLGuard+, a federated and lightweight anomaly detection framework specifically designed for 5G-enabled smart home ecosystems. The framework integrates edge AI with federated learning to detect network and device anomalies while preserving user privacy and reducing cloud dependency. A lightweight autoencoder-based model is trained across distributed edge nodes using privacy-preserving federated averaging. We evaluate our framework using the TON_IoT and CIC-IDS2018 datasets under realistic smart home attack scenarios. Experimental results show that Edge-FLGuard+ achieves high detection accuracy (\u226595%) with minimal communication and computational overhead, outperforming traditional centralized and local-only baselines. Our results demonstrate the viability of federated AI models for real-time security in next-generation smart home networks.<\/jats:p>","DOI":"10.3390\/fi17080329","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T15:19:22Z","timestamp":1753370362000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Edge-FLGuard+: A Federated and Lightweight Anomaly Detection Framework for Securing 5G-Enabled IoT in Smart Homes"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8872-5721","authenticated-orcid":false,"given":"Manuel J. C. S.","family":"Reis","sequence":"first","affiliation":[{"name":"Engineering Department and IEETA, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119462","DOI":"10.1109\/ACCESS.2023.3325929","article-title":"IoT Network Anomaly Detection in Smart Homes Using Machine Learning","volume":"11","author":"Sarwar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Suleiman, B., Alibasa, M.J., and Farid, F. (2024). Privacy-Aware Anomaly Detection in IoT Environments Using FedGroup: A Group-Based Federated Learning Approach. J. Netw. Syst. Manag., 32.","DOI":"10.1007\/s10922-023-09782-9"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chatterjee, A., and Ahmed, B.S. (2022). IoT Anomaly Detection Methods and Applications: A Survey. Internet Things, 19.","DOI":"10.1016\/j.iot.2022.100568"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3467981","article-title":"A Federated Learning Approach to Anomaly Detection in Smart Buildings","volume":"2","author":"Sater","year":"2021","journal-title":"ACM Trans. Internet Things"},{"key":"ref_5","unstructured":"Vasiljevic, P., Matic, M., and Popovic, M. (2025). Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","article-title":"A Survey of Network Anomaly Detection Techniques","volume":"60","author":"Ahmed","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Doshi, R., Apthorpe, N., and Feamster, N. (2018, January 24). Machine Learning DDoS Detection for Consumer Internet of Things Devices. Proceedings of the 2018 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA.","DOI":"10.1109\/SPW.2018.00013"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kikissagbe, B.R., and Adda, M. (2024). Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review. Electronics, 13.","DOI":"10.3390\/electronics13183601"},{"key":"ref_9","first-page":"367","article-title":"Advancements in IoT Anomaly Detection: Leveraging Machine Learning for Enhanced Security","volume":"Volume 189","author":"Vyas","year":"2025","journal-title":"Advances in Intelligent Systems Research, Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA 2025), Jaipur, India, 21\u201322 February 2025"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cultice, T., Ionel, D., and Thapliyal, H. (2020, January 14\u201316). Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network. Proceedings of the 2020 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), Chennai, India.","DOI":"10.1109\/iSES50453.2020.00026"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gosselin, R., Vieu, L., Loukil, F., and Benoit, A. (2022). Privacy and Security in Federated Learning: A Survey. Appl. Sci., 12.","DOI":"10.3390\/app12199901"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zeng, D., Luo, J., Xu, Z., and King, I. (May, January 30). A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy. Proceedings of the ACM Web Conference 2023, New York, NY, USA.","DOI":"10.1145\/3543873.3587681"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Albogami, N.N. (2025). Intelligent Deep Federated Learning Model for Enhancing Security in Internet of Things Enabled Edge Computing Environment. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-88163-5"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dritsas, E., and Trigka, M. (2025). Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications. J. Sens. Actuator Netw., 14.","DOI":"10.3390\/jsan14010009"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mughal, F.R., He, J., Das, B., Dharejo, F.A., Zhu, N., Khan, S.B., and Alzahrani, S. (2024). Adaptive Federated Learning for Resource-Constrained IoT Devices through Edge Intelligence and Multi-Edge Clustering. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-78239-z"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TSC.2018.2808956","article-title":"Low-Cost Adaptive Monitoring Techniques for the Internet of Things","volume":"14","author":"Trihinas","year":"2021","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Reis, M.J.C.S. (2025). Edge-FLGuard: A Federated Learning Framework for Real-Time Anomaly Detection in 5G-Enabled IoT Ecosystems. Appl. Sci., 15.","DOI":"10.3390\/app15126452"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Reis, M.J.C.S. (2025). AI-Driven Anomaly Detection for Securing IoT Devices in 5G-Enabled Smart Cities. Electronics, 14.","DOI":"10.3390\/electronics14122492"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Reis, M.J.C.S., and Ser\u00f4dio, C. (2025). Edge AI for Real-Time Anomaly Detection in Smart Homes. Future Internet, 17.","DOI":"10.3390\/fi17040179"},{"key":"ref_20","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A. (2017, January 10). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. PMLR."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The Algorithmic Foundations of Differential Privacy","volume":"9","author":"Dwork","year":"2013","journal-title":"FNT Theor. Comput. Sci."},{"key":"ref_22","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/8\/329\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:15:38Z","timestamp":1760033738000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/8\/329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":22,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["fi17080329"],"URL":"https:\/\/doi.org\/10.3390\/fi17080329","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]}}}