联邦医疗安全 (FedMedSecure):联合的短暂学习,具有交叉注意力机制和可解释的AI,用于协作医疗网络安全
Mohammed Tawfik1, Ashraf A Abu-Ein2,3, Hatem M Noaman2,3
1Faculty of Computer and Information Technology, Sana'a University, Sana'a, Yemen. kmkhol01@gmail.com.
Scientific reports
|November 14, 2025
概括
联邦医疗安全加强医疗物联网 (IoMT) 网络安全,使用联合的几次射击学习来保护隐私的威胁检测. 它实现了高精度并减少了通信,为医疗保健中可靠的AI建立了新的范式.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 越来越多地使用医疗物联网 (IoMT) 设备带来了重大的网络安全挑战.
- 现有的解决方案往往难以平衡先进的威胁检测与患者隐私保护.
研究的目的:
- 引入FedMedSecure,这是一个针对IoMT网络安全的联合的几次学习框架.
- 为了实现保护隐私的协作学习,可解释的AI和适应性威胁检测机制.
主要方法:
- 组合交叉变压器,FEAT,关系网络和规范化的MAML在一个信任权重组合中.
- 实现差异隐私和高效的梯度压缩,以减少通信.
- 根据CICIoMT2024和CIDC2017数据集进行评估.
主要成果:
- 实现了高准确性:在CICIoMT2024上达到99.9%,在监督学习中在CIDC2017上达到93.3%.
- 展示了强大的少数射击性能 (91.0-99.8%) 和联合学习准确性 (99.8%).
- 展示了强大的泛化和快速适应能力.
结论:
- 联邦医疗安全 (FedMedSecure) 提供协作式医疗网络安全,而不影响隐私.
- 在敏感领域建立了可信的人工智能新范式.
- 对金融服务,关键基础设施和政府网络提供更广泛的适用性.
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