Anomaly Detection in IoT Networks: Leveraging Federated Learning for Real-Time Threat Response

The impressive growth of the Internet of Things has produced considerable security challenges because of the heterogeneity of different devices and data volumes. This research article introduces an elaborate Internet of Things (IoT) security architecture which employs Federated Learning (FL) to address such challenges. The framework enhances IoT security by integrating federated learning with enhanced anomaly detection, better privacy-preserving mechanisms, and robust secured communication technology. It also pays special attention to preventing data exposure and minimizing the amount of communication done. The proposed architecture was evaluated in a synthesized IoT environment to put it against centralized security approaches. The one implemented in Florida also reached a detection accuracy of 93.7 %, in contrast to the previous report of 85.3% accuracy realised with centralised approaches. In addition, the framework was demonstrated to be highly efficient, especially in environments that lack resources where communication overhead was reduced by 250 MB to 180 MB. The application of privacy-preserving methodologies allowed us to score 9 on a scale of 10, whereas the regular integration of the approach has a score of 6. This underlines the increased protection of the details and information. The study shows that Federated Learning has the potential to significantly boost the security of IoT networks by offering a secure and privacy-preserving solution. The proposed framework is superior to the current methods in terms of precision and time and also in terms of data privacy. This work offers a good ground for the development and integration of FL-based security approaches in other IoT scenarios.