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穿戴式医疗设备的端到端隐私意识联合学习,通过可编程网络中的加密聚合进行编程

Huzaif Khan¹, Rahul Kavati¹, Sriven Srilakshmi Pulkaram¹

¹Department of Computer Science, California State University Dominguez Hills, 1000 E. Victoria Street, Carson, CA 90747, USA.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

边缘辅助同型联合学习 (EAH-FL) 通过实现安全的实时数据分析来增强医疗保健物联网. 这一框架平衡了下一代数字健康监测的隐私和效率.

科学领域:

医疗保健技术医疗保健技术医疗保健技术
物联网 (IoT) 的物联网 (IoT) 的物联网.
联合学习 (FL)

背景情况:

可穿戴的物联网设备可以实时监控医疗保健,但在响应性和数据隐私方面面临挑战.
联合学习 (FL) 提供了隐私,但遭受了推断攻击和通信开销.
网络内计算 (INC) 是高效的,但缺乏安全性,而同态加密 (HE) 则以延迟和可扩展性为代价提供隐私.

研究的目的:

引入边缘辅助同型联合学习 (EAH-FL),这是一个新的框架,将CKKS HE与网络内聚合结合起来.
通过提供一种实用的方法来解决现有解决方案的局限性,以确保医疗保健中的安全和高效的联合学习.
为了实现实时生理信号监控,增强隐私和低延迟.

主要方法:

开发了EAH-FL,这是一个整合CKKS HE与网络内聚合的框架.
客户端将加密/解密外包给边缘设备;可编程交换机在加密领域进行聚合.
使用现实的医疗保健数据集进行大规模模拟.

主要成果:

电子邮件和电子邮件服务中心 (EAH-FL) 保持了接近纯文本的模型准确性 (F1分数>0.93) 和高的数据包交付比率 (>0.95).
该框架在各种客户规模之间展示了有效的融合.
加密成本主要由边缘设备承担,而不是资源受限的可穿戴设备.

结论:

EAH-FL为医疗保健中的实时联合学习提供了一种实用,安全的解决方案,实现了强大的隐私,低延迟和可扩展性.
该框架是下一代数字健康监测系统的可行基石.
在医疗保健物联网中,EAH-FL有效地平衡了隐私,效率和性能之间的权衡.

关键词:

边缘计算是一种边缘计算.联合学习的联合学习.图形神经网络的神经网络医疗保健物联网物联网物联网同型型的加密方式.在网络内计算计算.

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