FedGraphHE:一个保护隐私的联合图形神经网络框架,具有动态同态加密和强大的聚合
Aocheng Zuo1, Zhanshen Feng2, Yuan Ping2
1School of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.
PloS one
|January 6, 2026
概括
与图形神经网络 (GNN) 联合学习增强了医疗保健AI. FedGraphHE引入了一种新的框架,使用同态加密来保护协作情报,提高准确性和降低成本,同时抵御攻击.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 允许跨设备的协作AI模型培训,而无需共享原始数据.
- 图形神经网络 (GNN) 对于分析医疗数据中的复杂关系非常有效.
- 现有的联合GNN正在与隐私漏洞,高计算成本和拜占庭式攻击作斗争.
研究的目的:
- 开发FedGraphHE,一个保护隐私的联合GNN框架,用于智能医疗保健中的安全协作智能化.
- 为了解决渐变隐私,计算开销和联合GNN中的拜占庭式攻击挑战.
主要方法:
- 三个协同模块的集成:动态自适应分区同型加密 (DAPHE) 进行优化梯度传输.
- 层次的多尺度自适应图形变压器 (HMAGT) 用于加密意识的图形处理.
- 通过同型内部产品 (FRAHIP) 进行联邦强大的聚合,用于拜占庭弹性聚合.
主要成果:
- 在引用网络基准 (Cora,CiteSeer,PubMed) 上,FedGraphHE的性能优于现有的保护隐私的方法.
- 在医学成像数据集 (ISIC 2020) 上实现了98.18%的分类准确性.
- 与同型加密基线相比,通信成本降低了约25%,在拜占庭式攻击下保持了>95%的准确性.
结论:
- FedGraphHE为医疗保健中对隐私敏感的协作学习提供了一个有效的解决方案.
- 该框架提高了智能医疗网络的诊断准确性和安全性.
- 与现有方法相比,在性能,效率和稳定性方面取得了显著的改进.
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