In this paper, we present VYRE, a ring-based oscillometric wearable designed for low-burden and robust frequent blood pressure monitoring. VYRE revisits the clinically established oscillometric method — widely accepted in arm and wrist form factors because of its high accuracy — and extends it to a compact ring form factor, currently realized as a proof-of-concept prototype. The key innovation is the ability to derive oscillometric signals on the finger by inducing controlled inflation and deflation to capture arterial oscillations in response to circumferential tension. This enables accurate estimation of blood flow dynamics within the digital arteries for blood pressure inference. VYRE leverages a lightweight model to estimate systolic and diastolic pressures from the measured oscillations observed from a vibration sensor integrated into the ring. Compared to PPG-based methods, this approach offers significant improvement in robustness to signal drift, ambient light variations, and skin tone differences. Each measurement requires a user-initiated ∼ 40-second quiet hold, after which the system returns systolic and diastolic readings without per-user calibration. In an IRB-approved study involving 71 participants, VYRE achieves mean absolute errors of 6.36 mmHg for systolic and 4.96 mmHg for diastolic pressure relative to a reference cuff, with biases of − 1.27mmHg and 0.0mmHg, respectively. The standard deviations of 7.87 mmHg (SBP) and 6.22 mmHg (DBP) meet the AAMI requirements (≤ 8mmHg), and 95% limits of agreement fall within [ − 16.69, 14.15] mmHg for systolic and [ − 12.2, 12.19] mmHg for diastolic pressure. Correlation with the reference is strong (Pearson r = 0.75 for SBP, r = 0.67 for DBP), confirming consistent tracking. The system maintained accuracy across finger sizes and hand postures. User feedback indicated that 84% of participants rated the device as comfortable or very comfortable, and 70% expressed willingness to use it daily. These results confirm VYRE’s practicality, accuracy, and potential as a compact, on-demand blood pressure monitoring solution.

ABSTRACT Objective To design a secure Federated Learning (FL) framework for Internet of Medical Things (IoMT) that protects sensitive patient data from both classical and quantum attacks. Methods Proposed the QSFedMA‐IoMT protocol integrating quantum and classical security techniques. Utilized entanglement‐based E91 protocol for generating a highly secure root key to establish trust. Applied BB84 protocol for efficient generation of per‐round session keys during FL updates. Incorporated classical cryptographic scheme AES‐GCM for secure communication. Employed privacy‐enhancing techniques such as norm‐clipping and Gaussian noise to mitigate information leakage during model training. Results Our work demonstrates robust resistance against both classical and quantum adversaries, while enhancing data privacy through secure key distribution and differential privacy mechanisms. It ensures the integrity of model updates within the federated learning process and achieves an effective balance between strong security guarantees and computational efficiency, making it well‐suited for IoMT environments. Conclusion The QSFedMA‐IoMT protocol delivers a robust and practical hybrid framework for securing federated learning in healthcare systems. By integrating E91 and BB84 protocols, it strengthens key management and trust establishment. The combination of quantum security with classical privacy‐preserving techniques ensures resilience, scalability, and efficiency. Overall, this work provides a promising direction for secure and privacy‐aware federated learning in next‐generation IoMT applications.