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.