Biometric Datasets for Federated Learning with Privacy and Integrity Constraints (SigD, BIDMC, TBME)

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.SigD contains digital signature dynamics captured from stylus-based handwriting on mobile devices. BIDMC provides photoplethysmography (PPG) recordings from intensive care unit patients, widely used in biomedical signal processing research. TBME comprises multi-session PPG signals collected in controlled environments for biometric verification studies.These datasets are used to simulate federated learning environments with realistic edge node distributions, emphasizing non-IID data, high-dimensional feature processing, and multi-class identity classification. Each dataset is preprocessed for federated simulation and annotated with standard metadata.

本数据集集合支持发表于题为《面向边缘计算中生物特征数据的隐私保护可验证联邦学习》（已提交至"IEEE Transactions on Knowledge and Data Engineering"）的手稿中的相关研究。其包含三份精心整理的生物特征数据集：SigD、BIDMC与TBME，用于评估BPVFL框架在隐私保护可验证联邦学习场景中的性能表现。SigD收录了从移动设备触控笔手写场景下采集的数字签名动力学特征数据。BIDMC提供了重症监护病房患者的光电容积描记（photoplethysmography, PPG）记录，该数据集已广泛应用于生物医学信号处理研究领域。TBME包含在受控环境中采集的多会话光电容积描记信号，适用于生物特征验证相关研究。上述数据集可用于模拟具备真实边缘节点分布的联邦学习环境，重点覆盖非独立同分布（non-IID）数据、高维特征处理以及多分类身份分类三类研究场景。每份数据集均针对联邦学习仿真完成了预处理，并标注有标准元数据。