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

新的隐私增强技术使人们能够对敏感、分布式、孤立和异类数据使用人工智能(AI)和高级数据分析。这一努力寻求通过采用联合学习(FL)和差分隐私(DP)来将新型隐私和人工智能技术过渡到医学成像领域。在这种方式下，敏感的成像数据可以保存在本地，只有模型可以共享和聚合，并提供强大的隐私保证。该实现的一个关键的新组成部分将是更好地适应数据异构性，减少DP-FL中训练诱导的偏差，并提高整体预测精度。这项工作将为皮肤病诊断建立和贡献网络基础设施，而现实的部署和评估承诺为其他也在敏感数据上运行的非医疗网络基础设施提供翻译影响。本研究将不同的私人联合学习(DP-FL)框架过渡到医学成像网络基础设施(MICI)，使皮肤病(如莱姆病)能够进行协作、智能、公平的诊断。从DP-FL的角度来看，关键的洞察力是精心制作的、不同隐私的数据增强技术，作为FL客户的医学图像的补偿。偏移量与本地FL模型一起优化，以缓解数据异构性，包括DP引入的数据异构性，从而提高诊断的准确性和公平性。从MICI的角度来看，这项研究为不同的现实世界场景量身定做了DP-FL，从患者的用例到医院的用例。也就是说，研究将跨设备DP-FL过渡到面向患者的移动应用，用于皮肤病的智能自我诊断，然后跨竖井DP-FL过渡到医院或医院部门参与的基于医院的诊断。与此同时，当同一患者的不同特征存在于不同的科室或医院时，垂直DP-FL也会过渡。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.