Saurab Singh

Federated Learning for Privacy-Preserving Anomaly Detection in Heterogeneous IoT Networks

Simulation of a Federated Learning framework for privacy-preserving anomaly detection tailored to heterogeneous IoT networks characterised by non-independent and identically distributed data, variable computational capacities, and intermittent connectivity indicates that the proposed method offers a practical, scalable, and regulation-compliant pathway toward trustworthy intrusion and anomaly detection in large-scale, heterogeneous IoT deployments.