The expansion of Internet of Things (IoT) and Internet of Medical Things (IoMT) infrastructures has increased the generation of multivariate sensor streams that reflect complex operational behaviors in industrial and clinical environments. Centralized anomaly detection approaches face limitations in IoMT due to privacy constraints, latency, and device heterogeneity. Federated learning (FL) enables distributed model training without data centralization; however, its behavior under highly non-Inde