Biometric Datasets for Federated Learning with Privacy and Integrity Constraints (SigD, BIDMC, TBME)
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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.
本数据集合集支持发表于题为《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》）的手稿相关研究。该合集包含三个经过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包含从移动设备触控笔书写过程中采集的数字签名动力学数据。BIDMC提供了重症监护病房患者的光电容积描记术（photoplethysmography, PPG）记录，该数据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时段PPG信号，用于生物特征验证相关研究。上述数据集被用于模拟具备真实边缘节点分布的联邦学习环境，重点关注非独立同分布（non-IID）数据、高维特征处理以及多分类身份分类任务。每个数据集均经过联邦学习模拟预处理，并标注有标准元数据。
