Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach

2026-06-12

AI Summary

This work addresses the dual challenges faced by resource-constrained Internet of Medical Things (IoMT) devices under the threat of quantum computing: the obsolescence of classical cryptography and privacy leakage in federated learning. To this end, the authors propose an edge-native, post-quantum secure federated learning framework that uniquely integrates lightweight post-quantum cryptography (PQC), distributed cryptographic processing, and Kubernetes-based edge orchestration to enable low-latency, low-overhead secure communication and model updates. Experimental evaluation on a Raspberry Pi platform demonstrates that the proposed distributed cryptographic processing significantly reduces latency compared to a serial implementation while maintaining acceptable resource consumption, thereby validating the system’s feasibility in achieving quantum-safe security, privacy preservation, and scalability.

Abstract

Internet of Medical Things (IoMT) devices operate under strict resource constraints while handling highly sensitive health data, making security and privacy critical concerns. Federated learning (FL) further complicates this landscape, as model updates exchanged during training may unintentionally expose private medical information. Emerging quantum computing capabilities threaten the long-term viability of conventional lightweight cryptographic mechanisms, motivating the integration of Post-Quantum Cryptography (PQC) into IoMT systems. This article discusses key enabling technologies for quantum-resilient IoMT, including post-quantum key establishment, lightweight encryption, and edge-native orchestration. We propose a scalable Kubernetes-based framework that integrates PQC into FL-enabled IoMT environments and validate it on a Raspberry Pi testbed. Results demonstrate that distributed cryptographic processing significantly reduces latency compared to sequential designs while maintaining feasible resource overhead. The primary contribution of this work lies in the design and validation of a secure orchestration and communication framework for FL-enabled IoMT systems. We conclude by outlining future directions toward energy-aware architectures, intelligent security optimization, and resilient next-generation Intelligent Internet of Medical Things (IIoMT) ecosystems.

Problem

• Internet of Medical Things
• Post-Quantum Cryptography
• Federated Learning
• Security and Privacy
• Quantum Computing Threat

Innovation

• Post-Quantum Cryptography
• Federated Learning
• Edge-Native Orchestration
• IoMT Security
• Lightweight Encryption