DDP-FedFV:双解个性化联合学习框架,用于识别手指静脉
Zijie Guo1,2, Jian Guo1,2, Yanan Huang2,3
1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
这项研究引入了双解个性化联合学习框架 (DDP-FedFV) 用于指纹静脉识别. 该方法提高了通用性和个性化,优于没有隐私风险的集中式模型.
科学领域:
- 生物识别信息 生物识别信息
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 指纹识别为身份验证提供了很高的准确性,但在集中式方法中面临隐私问题.
- 联合学习 (FL) 通过培训没有数据共享的模型来解决数据隐私问题,但其性能受到数据集异质性的影响.
- 现有的FL方法难以平衡全球模型通用性与个人客户模型个性化.
研究的目的:
- 提出一个新的联合学习框架,DDP-FedFV,专门设计用于指纹静脉识别.
- 提高全球模型的通用性和客户端模型在分布式环境中的个性化.
- 解决生物识别应用中的数据异质性引起的联合学习的性能限制.
主要方法:
- 引入了双解机制 (模型和特征解),以优化特征表示和全球模型通用性.
- 实施了个性化权重聚合方法 (FedPWRR),以根据数据分布量身定制客户模型.
- 通过对六个公开的手指静脉数据集进行理论分析和实验,评估了DDP-FedFV框架.
主要成果:
- DDP-FedFV框架有效地结合了指纹静脉识别的概括和个性化.
- 双解机制改善了特征表示和全球模型通用性.
- 通过优化基于数据分布的参数聚合,FedPWRR增强了客户端模型个性化.
- 实验结果显示,与传统的集中式培训模式相比,其表现优越.
结论:
- 拟议的DDP-FedFV框架为手指静脉识别提供了一种保护隐私和有效的解决方案.
- 双解和个性化聚合策略成功地解决了在异质生物识别数据中的FL挑战.
- 在不影响数据隐私或增加通信开销的情况下,DDP-FedFV实现了高精度.
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