联邦学习+后量子医疗数据保护
IoMT设备资源受限且处理敏感健康数据，联邦学习中模型更新可能泄露隐私。量子计算威胁传统加密，需集成后量子密码（PQC）。该文提出基于Kubernetes的框架，在Raspberry Pi测试床上验证。分布式加密处理延迟比顺序设计低32%，资源开销可控。框架为联邦学习IoMT系统提供安全编排与通信方案。
Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach
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.