MedSparseFL: Sparse and Privacy-Preserving Federated Learning for Healthcare Applications

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This project provides processed data indexes for reproducing experiments from the MedSparseFL study, a sparse and privacy-preserving federated learning framework for healthcare applications. The datasets include CheXpert and HAM10000. For each dataset, CSV files (chexpert_index.csv and ham10000_index.csv) contain:Image IDs referencing the official dataset filesLabels used in classification tasksTrain/Validation/Test splits for experiment reproducibility