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

CICI：TCR：转变差异化私有联合学习，以实现医学影像网络基础设施上的协作、智能、公平的皮肤病诊断

• 批准号: 2319742
• 负责人: Yinzhi Cao
• 金额: $ 120万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319742&HistoricalAwards=false
• 关键词: CICI; TCR; Transitioning; Differentially; Private

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.

新的隐私增强技术可以在敏感，分布式，孤立和异构数据上使用人工智能（AI）和高级数据分析。 这项努力通过采用联合学习（FL）以及差异隐私（DP），将新颖的隐私和AI技术转变为医学成像领域。 以这种方式，敏感的成像数据可以保存本地，并且只有强大的隐私保证可以共享和汇总模型。 实施的一个关键新组成部分是更好地适应数据异质性，减少训练引起的DP-FL偏差并提高总体预测准确性。 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作为莱姆病。从DP-FL的角度来看，关键见解是一种精心制作的私人数据增强技术，可抵消FL客户的医学图像。偏移量与本地FL模型一起优化，以减轻数据异质性，包括DP引入的数据异质性，从而提高了诊断精度和公平性。从MICI的角度来看，该研究对DP-FL定制了不同现实情况的DP-FL，从患者的用例到医院的情况。也就是说，研究将跨设备DP-FL转变为以患者为导向的移动应用程序，以进行皮肤疾病的智能自我诊断，然后跨科学DP-FL到基于医院的诊断，医院或医院部门参与其中。同时，当在不同部门或医院之间存在同一患者的不同特征时，垂直DP-FL也会过渡。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估值得支持。