CICI: TCR: Transitioning Differentially Private Federated Learning to Enable Collaborative, Intelligent, Fair Skin Disease Diagnostics on Medical Imaging Cyberinfrastructure

New privacy enhancing technologies are enabling the use of Artificial Intelligence (AI) and advanced data analytics on sensitive, distributed, siloed, and heterogeneous data. This effort seeks to transition novel privacy and AI technologies to the domain of medical imaging by adopting Federated learning (FL) together with Differential Privacy (DP). In this fashion, sensitive imaging data can be kept local and only models are shared and aggregated with strong privacy guarantees. A key novel component of the implementation will be to better accommodate data heterogeneity, reduce training-induced bias in DP-FL, and improve overall prediction accuracy. The work will build and contribute to cyberinfrastructure for skin disease diagnosis, while the realistic deployment and evaluation promises to provide translational impact to other non-medical cyberinfrastructure also operating on sensitive data. This research transitions differentially private federated learning (DP-FL) framework to medical imaging cyberinfrastructure (MICI) with the enabling of collaborative, intelligent, fair diagnostics of skin diseases, such as Lyme diseases. From the DP-FL perspective, the key insight is a carefully crafted, differentially private data augmentation technique that serves as an offset to medical images at FL clients. The offset is optimized together with local FL models to mitigate data heterogeneity including those introduced by DP, thus improving diagnostics accuracy and fairness. From the MICI perspective, the research tailors DP-FL for different real-world scenarios ranging from use cases for patients to those for hospitals. That is, the research transitions cross-device DP-FL to patient-oriented mobile applications for intelligent self-diagnostics of skin diseases, and then cross-silo DP-FL to hospital-based diagnostics in which a hospital or hospital department participates. At the same time, vertical DP-FL is also transitioned when different features of the same patient exist across different departments or hospitals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.