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Updated: Jan 13, 2026

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联邦自主监督的少数镜头人脸识别系统
Nursultan Makhanov1, Beibut Amirgaliyev1, Talgat Islamgozhayev1
1Smart City Research Center, Astana IT University, Astana 010000, Kazakhstan.
Journal of imaging
|October 28, 2025
概括
面部识别的联合学习保护了隐私,但降低了12-30%的准确性. 传统的CNN在这个联合的少数射击学习环境中表现比变压器更好.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 保护隐私的机器学习对于像面部信息这样的敏感数据至关重要.
- 联合学习 (FL),自主监督学习 (SSL) 和少量学习 (FSL) 是先进的技术,具有保护隐私的面部识别潜力.
研究的目的:
- 开发和评估一个系统的框架,结合FL,SSL和FSL,用于保护隐私的面部识别.
- 量化与面部识别系统中隐私保护相关的性能权衡.
主要方法:
- 在联合环境中,在CASIA-WebFace数据集上使用SimCLR进行自我监督的预训.
- 在使用原型网络的LFW数据集上进行了联合的少数镜头微调.
- 在六个架构的全面评估:ResNet,DenseNet,MobileViT,ViT-Small,CvT和CoAtNet.
主要成果:
- 联合框架成功地保护了数据隐私,但与集中式方法相比,它会导致显著的性能下降 (12-30%的准确性损失).
- 卷积神经网络 (CNN) 在联邦制约下表现出比基于变压器的架构更强大的稳定性.
- 五拍式学习配置提供了数据效率和识别性能之间的最佳平衡.
结论:
- 联邦面部识别中的隐私保护具有相当大的性能成本.
- 对于联合的几次射击面部识别任务,CNN比变压器更适合.
- 提供了用于部署保护隐私的面部识别系统的实证基准和见解,突出了关键的隐私-实用性权衡.
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