This $1,200,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports The Johns Hopkins University in transitioning differentially private federated learning (DP-FL) to enable collaborative, intelligent, and fair skin disease diagnostics on medical imaging cyberinfrastructure (MICI). The key outcomes will be to improve the accuracy and fairness of skin disease diagnosis through the adoption of DP-FL, which allows sensitive imaging data to remain local while only models are shared and aggregated with strong privacy guarantees. The research aims to better accommodate data heterogeneity, reduce training-induced bias in DP-FL, and enhance overall prediction accuracy for skin disease diagnostics. This work will build and contribute to MICI for skin disease diagnosis, while also providing translational impact to other non-medical cyberinfrastructure operating on sensitive data.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $1.2m | 7/28/23 |