为医疗物联网设计抗量子攻击的边缘联邦学习框架
Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach
- 在边缘设备上集成抗量子加密，实现安全联邦学习
- 基于Kubernetes的分布式架构使延迟降低，资源开销可控
- 适合关注医疗数据隐私与未来量子安全的开发者
物联网医疗设备在资源受限条件下处理高度敏感的健康数据，安全与隐私问题尤为突出。联邦学习（FL）虽能保护数据不外泄，但模型更新可能无意泄露私密信息。量子计算的发展威胁传统轻量级加密机制的长期有效性，促使将后量子密码学（PQC）引入IoMT系统。本文探讨了构建抗量子医疗物联网的关键技术，包括后量子密钥协商、轻量加密及边缘原生编排。提出一种基于Kubernetes的可扩展框架，将PQC融入联邦学习支持的IoMT环境，并在Raspberry Pi测试平台上验证。结果表明，分布式加密处理相比串行设计显著降低延迟，同时保持合理的资源开销。主要贡献在于设计并验证了一个安全的联邦学习式医疗物联网通信与编排框架。最后，展望了面向节能的架构、智能安全优化以及下一代智能医疗物联网生态系统的演进方向。
原文摘要 · Abstract (English)
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.
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