智能医疗保健中的联合学习:对隐私,安全和预测分析与物联网集成的全面审查
Syed Raza Abbas1, Zeeshan Abbas2,3, Arifa Zahir1
1Department of Bioscience, COMSATS University, Islamabad 45550, Pakistan.
Healthcare (Basel, Switzerland)
|January 8, 2025
概括
联合学习 (FL) 通过在各机构之间实现协作机器学习,同时保持患者隐私,从而提高医疗保健. 解决安全性和数据异质性等挑战是其采用改善患者结果的关键.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 机器学习 机器学习
- 数据隐私 数据隐私
背景情况:
- 联合学习 (FL) 能够实现跨机构的协作机器学习,这对于医疗数据分析至关重要.
- 患者隐私和监管合规在医疗数据处理中至关重要.
研究的目的:
- 审查FL在智能健康系统中的应用,重点关注物联网,可穿戴设备和远程监控.
- 识别和讨论与医疗保健中FL相关的挑战和保护隐私的解决方案.
主要方法:
- 关于智能医疗保健中的联合学习应用的文献综述.
- 分析安全风险,数据异质性,可扩展性和互操作性问题.
- 检查隐私保护技术,如差异隐私和安全的多方计算.
主要成果:
- FL与物联网,可穿戴设备和远程监控集成,用于实时的分散数据处理.
- 主要挑战包括安全威胁 (敌对攻击,数据中毒),数据异质性,可扩展性和互操作性.
- 新兴的解决方案,如差异隐私和安全的多方计算,对于FL的成功至关重要.
结论:
- 联合学习对安全,数据驱动的医疗保健具有变革性的潜力.
- 克服FL挑战对于提高医疗保健的效率,准确性和采用性至关重要.
- FL承诺改善患者的治疗结果,提高运营效率和数据主权.
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