一个联合的增量区块链框架,具有保护隐私的XAI优化,用于保护医疗保健数据
Tanisha Bhardwaj1, K Sumangali2
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India.
Scientific reports
|October 31, 2025
概括
这项研究介绍了一种新的保护隐私的联合增量学习系统,该系统是通过区块链和可解释的人工智能进行优化. PPFILB-OXAI模型提高了用于医疗数据分析的机器学习的安全性和效率.
科学领域:
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
- 区块链技术 区块链技术
- 人工智能的人工智能
背景情况:
- 联合学习 (FL) 提供隐私优势,但在平衡模型安全性,数据隐私和系统效率方面面临挑战.
- 现有的FL实现通常会牺牲效率,以提高隐私,可追溯性,可解释性和安全性.
- 需要综合解决方案来优化分布式机器学习中的这些竞争因素.
研究的目的:
- 开发一个以区块链和可解释AI (PPFILB-OXAI) 优化的保护隐私的联合增量学习系统.
- 提高机器学习模型在处理敏感医疗数据时的安全性,隐私和效率.
- 引入混乱猫优化算法 (CBOA),用于在可解释的AI中进行特征选择.
主要方法:
- 利用区块链来实现安全和透明的医疗数据处理.
- 实施联合增量学习 (FIL) 以实时适应和从客户端数据中提取模式.
- 将可解释的AI (XAI) 与混乱的猫优化算法 (CBOA) 集成,以获得最佳的特征选择和模型可解释性.
- 利用对异常模型的聚合过器和用于攻击分类的透深度信念网络 (EDBN).
主要成果:
- PPFILB-OXAI系统在心脏病和乳腺癌威斯康星州数据集的精度,回忆,f-测量和准确性方面表现出卓越的表现.
- 对于心脏病预测,该系统实现了94.87%的精度,96.73%的回忆,95.79%的f测量和95.71%的准确性.
- 对于威斯康星州的乳腺癌预测,该系统实现了97.13%的精度,97.70%的回忆,97.41%的f测量和96.84%的准确性.
结论:
- 拟议的PPFILB-OXAI系统有效地平衡了医疗应用的联合学习中的隐私,安全和效率.
- 区块链,FIL,XAI和CBOA的整合为保护隐私的机器学习提供了一个强大的框架.
- 该系统显示了改善医疗保健诊断准确性和模型透明度的巨大潜力.
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