动态个性化联合学习用于跨光谱的手指纹识别
概括
本研究介绍了一种动态个性化联合学习模型 (DPFed-Palm),用于安全的跨光谱手指纹识别. 这种新的方法增强了隐私,并通过解决数据分布挑战来提高识别准确性.
科学领域:
- 生物识别信息 生物识别信息
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 掌印识别提供了高精度,稳定性和安全性,但在集中式深度学习中面临隐私问题.
- 非独立且相同分布的 (非IID) 多光谱手掌纹数据会降低识别性能.
研究的目的:
- 提出一个动态的个性化联合学习模型 (DPFed-Palm),用于保护隐私的跨光谱手指纹识别.
- 解决数据隐私和非IID数据在多光谱手指纹识别中的挑战.
主要方法:
- 开发了一种新的损失函数组合,用于有效的本地模型训练和增强的特征表示.
- 实施了混合聚合策略,结合了联邦平均 (FedAvg) 和个性化联合学习 (PFL).
- 通过跨光谱测试引入了一个动态的重量选择策略,以实现最佳的个性化全球模型选择.
主要成果:
- 与现有方法相比,DPFed-Palm展示了优越的隐私保护能力.
- 拟议的模型实现了公开数据集 (PolyU,IITD,CASIA) 的增强识别性能.
- 实验结果验证了动态重量选择和混合聚合策略的有效性.
结论:
- DPFed-Palm有效地提高了跨光谱手指纹识别中的隐私和识别性能.
- 该模型成功地缓解了与非IID数据和集中式培训在联合学习环境中的集中式培训相关的问题.
- 这种方法为安全和准确的生物识别系统提供了一个有希望的解决方案.
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