Adaptive Three-Tier Federated Intelligence for Real-Time Anomaly Detection and Threat Prediction in Resource-Constrained IoT Systems

Abstract

The blistering development of the Internet of Things (IoT) systems has made them more vulnerable to the cyber threat, especially when it comes to resources and the decentralization of data production. This paper suggests a three-level federated intelligence model, which is flexible in real-time anomaly detection and predictive threat analysis of IoTs. The model combines on-device lightweight detection, edge-level federated learning and cloud-based global optimization to facilitate privacy-preserving and scalable security. A resource conscious adaptive feature selection system is used to dynamically scale the computational complexity, and a time prediction algorithm used to predict early threats. To ensure realistic validation of the model in various attack scenarios, benchmark IoT intrusion detection datasets are used to evaluate the model, which are widely used in federated anomaly detection research. The experimental evidence indicates that the offered method can reach the accuracy of 96.8% and F1-score of 96.0% and decrease the volume of communication by 85% and the execution time to 18 ms, instead of 42 ms. Moreover, the prediction module attains 93.7% accuracy and has a lead time of 6.8 seconds. These results point to the proposed framework as being a successful balance of accuracy, efficiency, and scalability to real-world IoT security applications.