FedHealthFog:一种支持学习的联合方法,通过雾计算平台对医疗保健分析进行分析
Subhranshu Sekhar Tripathy1, Sujit Bebortta2, Chiranji Lal Chowdhary3
1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, India.
Heliyon
|March 12, 2024
概括
本研究介绍了FedHealthFog,这是一个用于医疗保健物联网系统的新型联合学习 (FL) 框架. 通过使用雾节点进行本地数据聚合,FedHealthFog提高了隐私和效率,大大降低了延迟和能源使用.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 分布式计算 分布式计算
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 医疗保健中的联合学习 (FL) 在各种计算平台上增强了患者数据隐私.
- 由于资源有限,物联网医疗保健系统面临着传统FL的挑战,因为计算和通信成本很高.
- 现有的FL模型因集中数据聚合而面临效率低下的培训问题.
研究的目的:
- 在资源有限的医疗保健物联网中引入FedHealthFog框架,以实现高效,保护隐私的联合学习.
- 解决传统FL的局限性,特别是高延迟和能源消耗.
- 在延迟敏感的医疗保健应用中优化分布式学习.
主要方法:
- 开发了FedHealthFog框架,在FL架构中使用雾节点作为本地聚合器.
- 实施了一个贪的启发式技术,以选择最佳的雾节点进行全球聚合.
- 对关键绩效指标的基准算法进行评估FedHealthFog.
主要成果:
- 在FedHealthFog中,实现了通信延迟 (高达87.01%) 和能源消耗 (高达57.98%) 的显著降低.
- 与最先进的替代方案相比,该框架显示了效率的提高.
- 减少了模型培训所需的全球聚合周期的数量.
结论:
- 在资源有限的医疗保健物联网环境中,FedHealthFog有效地克服了挑战.
- 该框架为保护隐私和高效的联合学习提供了一个有希望的解决方案.
- 在物联网生态系统中,FedHealthFog有可能彻底改变对延迟敏感的应用程序.
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