A simulation study of differentially private federated learning with zero-trust policy screening for medical IoT devices

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Artificial intelligence is increasingly used in connected medical-device systems for physiological-signal classification and continuous monitoring. This paper presents a simulation study of the learning and policy layers of a zero-trust federated-learning framework for Internet of Medical Things (IoMT) devices. The implemented prototype combines federated averaging, Gaussian perturbation of client updates, a device-level trust score, and gradient-magnitude screening of anomalous client updates. This is a simulation study of the federated-learning and zero-trust policy layers; cryptographic, encrypted-transport, and hardware-attestation components are modeled as deployment considerations or overheads and are not implemented as complete physical or protocol-level modules. Using the ECG classification pipeline described in the methodology, we compare centralized training, traditional federated learning, differentially private federated learning, and the proposed zero-trust federated configuration. The proposed ZT-FL configuration is reported at 0.928 accuracy, 0.920 F1-score, and 0.968 AUC. These results support feasibility within the stated simulation scope and do not establish clinical validity, formal end-to-end DP accounting, comprehensive adversarial robustness, regulatory compliance, or post-quantum readiness.