The high rate of Internet of Things (IoT) device proliferation in smart cities, along with the emergence of 6G technology, has tremendously augmented the network traffic and the issue of security. This paper proposes a Federated Learning-based Intrusion Detection System (FL-IDS) specifically designed for 6G-enabled IoT networks. The new system helps to solve the problem of scalability, privacy protection, and the possibility of detecting anomalies in time without any central storage of the data. FL allows training local models on edge devices and only the weights are transferred to a central model, which allows preserving sensitive information privacy. The methodology involves the use of intrusion detection using the local models of Random Forest, SVM, and KNN, and trained locally on the IoT devices. These models are subsequently federated by averaging to create a federated global model to be effective in detecting intrusion in large-scale IoT networks. The system identifies the anomalies of Denial of Service (DoS), spoofing, and data breach based on the network traffic patterns and device behavior variation. The system was evaluated using key performance metrics, namely, accuracy, precision, recall, and F1-score. These findings prove that the FL-IDS can attain an accuracy of 98, an increase of 12 % over the traditional intrusion detection systems. The system also decreases false positive rates by 20 %, as well as communication overhead by 35 %. The federated learning architecture enables scalable and efficient deployment, a large amount of data processing, and data privacy. Finally, the FL-based intrusion detection system provides a solution that is privacy-saving, scalable, and real-time to detect intrusion in 6G IoT networks in smart cities. The further study will concentrate on the optimization of the model updates and the system performance in dynamic urban settings, as well as real-time monitoring.
Kalyanasundaram K, Sivakolundu V, Mohan J. Enhancing IoT Security in 6G Networks Using Federated Learning: A Decentralized Approach. 2025;107–34.
2.
Bamal S, Singh L. Detecting Conjunctival Hyperemia Using an Effective Machine Learning based Method. Journal of Internet Services and Information Security. 2024;14(4):499–510.
3.
Pelekoudas‐Oikonomou F, Mirzaee PH, Hathal W, Mantas G, Rodriguez J, Cruickshank H, et al. Federated Learning‐Based Intrusion Detection Systems for Massive IoT. Security and Privacy for 6G Massive IoT. Wiley; 2024. p. 101–28.
4.
Bahmaid DrS, Mahyoub Ghaleb DrSA. Intrusion Detection System Using Chaotic Walrus Optimization-based Convolutional Echo State Networks for IoT-assisted Wireless Sensor Networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2024;15(3):236–52.
5.
Kim M, Oh I, Yim K, Sahlabadi M, Shukur Z. Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies. IEEE Access. 2024;12:33972–4001.
6.
Rojas C, García F. Optimizing traffic flow in smart cities: A simulation-based approach using IoT and AI integration. Association Journal of Interdisciplinary Technics in Engineering Mechanics. 2024;(1):19–22.
7.
Bhavsar MH, Bekele YB, Roy K, Kelly JC, Limbrick D. FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT. IEEE Access. 2024;12:52215–26.
8.
Garroppo RG, Giardina PG, Landi G, Ruta M. Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings. Future Internet. 2025;17(5):191.
9.
Rashid MM, Khan SU, Eusufzai F, Redwan MdA, Sabuj SR, Elsharief M. A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks. Network. 2023;3(1):158–79.
10.
Ferrag MA, Friha O, Kantarci B, Tihanyi N, Cordeiro L, Debbah M, et al. Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses. IEEE Communications Surveys & Tutorials. 2023;25(4):2654–713.
11.
Sirohi D, Kumar N, Rana PS, Tanwar S, Iqbal R, Hijjii M. Federated learning for 6G-enabled secure communication systems: a comprehensive survey. Artificial Intelligence Review. 2023;56(10):11297–389.
12.
Kalodanis K, Papapavlou C, Feretzakis G. Enhancing Security in 5G and Future 6G Networks: Machine Learning Approaches for Adaptive Intrusion Detection and Prevention. Future Internet. 2025;17(7):312.
13.
Alsamiri J, Alsubhi K. Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions. Future Internet. 2023;15(12):403.
14.
Alterkawi L, Dib FK. Federated Learning for Smart Cities: A Thematic Review of Challenges and Approaches. Future Internet. 2025;17(12):545.
15.
Kianpisheh S, Taleb T. Collaborative Federated Learning for 6G With a Deep Reinforcement Learning-Based Controlling Mechanism: A DDoS Attack Detection Scenario. IEEE Transactions on Network and Service Management. 2024;21(4):4731–49.
16.
Hafi H, Brik B, Frangoudis PA, Ksentini A, Bagaa M. Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions. IEEE Access. 2024;12:9890–930.
17.
Sarvakar K, Prajapati D, Contreras F. Machine and Deep Learning-Based Anomaly Detection in 6G Smart Cities. Secure Communication for the 6G and the Internet of Things Networks. 2025;54–87.
18.
Rani P, Sharma C, Ramesh J, Verma S, Sharma R, Alkhayyat A, et al. Federated learning-based misbehavior detection for the 5g-enabled internet of vehicles. 2023;(2):4656–64.
19.
Jithish J, Mahalingam N, Wang B, Yeo KS. Towards enhancing security for upcoming 6G-ready smart grids through federated learning and cloud solutions. Cybersecurity. 2025;8(1).
20.
Alsaleh SS, El Bachir Menai M, Al-Ahmadi S. Federated Learning-Based Model to Lightweight IDSs for Heterogeneous IoT Networks: State-of-the-Art, Challenges, and Future Directions. IEEE Access. 2024;12:134256–72.
21.
Alotaibi A, Barnawi A. IDSoft: A federated and softwarized intrusion detection framework for massive internet of things in 6G network. Journal of King Saud University - Computer and Information Sciences. 2023;35(6):101575.
22.
Shindagi KS, Koppad KV, Ekbote PR, Bhandare NK, Revankar DD, Sonwalkar P, et al. Federated Learning for Enhancing Cybersecurity in IoT-Integrated 6G Networks: Challenges, Opportunities, and Future Directions. Advanced Sciences and Technologies for Security Applications. Springer Nature Switzerland; 2025. p. 249–62.
23.
Jai Vinita L, Vetriselvi V. Federated Learning-based Misbehaviour detection on an emergency message dissemination scenario for the 6G-enabled Internet of Vehicles. Ad Hoc Networks. 2023;144:103153.
24.
Kurshumova D. Weighing The Pros and Cons of Artificial Intelligence (Ai) In Higher Education: A Mixed-Methods Survey of Bulgarian University Instructors. International Online Journal of Education and Teaching. 2025;(2):12–28.
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