NSF Convergence Accelerator Track D: A Trusted Integrative Model and Data Sharing Platform for Accelerating AI-Driven Health Innovation

NSF 融合加速器轨道 D：加速人工智能驱动的健康创新的可信集成模型和数据共享平台

• 批准号: 2040588
• 负责人: Hai Li
• 金额: $ 96.61万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040588&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Trusted

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This project, NSF Convergence Accelerator Track D: A Trusted Integrative Model and Data Sharing Platform for Accelerating AI-Driven Health Innovation, will develop a novel health-related federated learning and model-sharing platform, LEARNER, to enable collaborative big data mining for biomedical applications by integrating cross-disciplinary expertise from machine learning, trustworthy AI, and biomedical data science. LEARNER will incorporate novel asynchronous federated learning algorithms based on rigorous theoretical foundations using trustworthy AI techniques, fairness-aware and interpretable machine learning models, large-scale computational strategies and effective software tools to reveal the complex relationships among heterogeneous health data. The project will address critical challenges in exploiting big data for biomedical and health, which include access to large data collections, computational intensity of AI/ML algorithms, complexity of hyperparameter tuning, and the need for effective multidisciplinary expertise and collaboration. Data privacy is another critical concern since health data is intrinsically sensitive and could be exploited to reveal an individual’s identity even when the data are carefully anonymized. LEARNER will include a suite of collaborative data analysis and privacy-preserving mechanisms and tools that will securely support various types of health data analytics, including mechanisms to detect potential data privacy leakages. Machine learning models typically involve complex procedures for optimization and the induced results can be difficult to interpret, and to replicate and reproduce. Novel methods will be employed to improve the interpretability and reproducibility of complex health data analytics models.The project team, with individuals from academia and industry, will develop an interdisciplinary program for training and education of graduate and undergraduate students. A cross-disciplinary course will also be developed on Health Data Science for beginning graduate students and senior undergraduate students from a variety of programs, including Computer Science and Engineering, Informatics, Electrical Engineering, Biomedical Engineering, Biology, and Statistics. The project will put special emphasis on attracting female and under-represented minority students to explore advanced computational technologies in the context of the LEARNER platform. Interested senior undergraduate students will be able to work on well-defined and well-scoped small projects, which will enable them to work with graduate students and the PI team of the project. Such project could also be undertaken as summer projects by undergraduate students in science and engineering.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.

NSF 融合加速器支持基于使用的、基于团队的多学科努力，以解决国家重大挑战，并将在不久的将来为社会提供有价值的成果。该项目，NSF 融合加速器轨道 D：可信的集成模型和数据共享。加速人工智能驱动的健康创新平台将开发一个新型的健康相关联合学习和模型共享平台LEARNER，通过整合跨学科专业知识，实现生物医学应用的协作大数据挖掘来自机器学习、可信人工智能和生物医学数据科学的 LEARNER 将结合基于严格理论基础的新型异步联邦学习算法，使用可信人工智能技术、公平感知和可解释的机器学习模型、大规模计算策略和有效的软件工具来揭示。该项目将解决利用大数据进行生物医学和健康方面的关键挑战，其中包括访问大数据集合、人工智能/机器学习算法的计算强度、超参数调整的复杂性以及有效性的需求。多学科专业知识和协作是另一个关键问题，因为健康数据本质上是敏感的，即使数据被仔细匿名化，也可能被利用来泄露个人身份。这将安全地支持各种类型的健康数据分析，包括检测潜在数据隐私泄露的机制。机器学习模型通常涉及复杂的优化过程，并且所产生的结果可能难以解释，并且将采用新颖的方法。提高可解释性项目团队将与来自学术界和工业界的人士一起开发一个跨学科项目，用于研究生和本科生的培训和教育，还将开发一个关于健康数据科学的跨学科课程。该项目将特别注重吸引女性和代表性不足的少数族裔学生探索先进的计算。背景下的技术感兴趣的高年级本科生将能够从事定义明确、范围明确的小型项目，这将使他们能够与研究生和该项目的 PI 团队合作，这样的项目也可以在暑期进行。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Communication-Efficient Projection-Free Algorithm for Nonconvex Constrained Learning Models

非凸约束学习模型的通信高效无投影算法

• 发表时间: 2021
• 期刊: 35th AAAI Conference on Artificial Intelligence (AAAI 2021
• 作者: Wenhan Xian, Feihu Huang

A Faster Decentralized Algorithm for Nonconvex Minimax Problems

• 发表时间: 2021
• 作者: Wenhan Xian;Feihu Huang;Yanfu Zhang;Heng Huang

Step-Ahead Error Feedback for Distributed Training with Compressed Gradient

• DOI: 10.1609/aaai.v35i12.17254
• 发表时间: 2020-08
• 作者: An Xu;Zhouyuan Huo;Heng Huang

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

• DOI: 10.1109/cvpr52688.2022.02020
• 发表时间: 2022-03
• 期刊: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: An Xu;Wenqi Li;Pengfei Guo;Dong Yang;H. Roth;Ali Hatamizadeh;Can Zhao;Daguang Xu;Heng Huang;Ziyue Xu-

On the Convergence of Communication-Efficient Local SGD for Federated Learning

• DOI: 10.1609/aaai.v35i9.16920
• 发表时间: 2021-05
• 作者: Hongchang Gao;An Xu;Heng Huang