NIH R01 · 2025
Project Summary Federated learning has emerged as a promising technique in biomedical research, providing the potential to construct robust common machine learning models with datasets from multiple institutions without having to share data among groups. However, the current implementations of this technique present several challenges that must be addressed before it can be widely adopted in the field of biomedicine. These challenges include issues related to bias and data heterogeneity, privacy and security, and interoperability among institutions. To mitigate these challenges, it is essential to integrate domain-specific knowledge with the theoretical advances in computer science and…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.