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融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。该项目名为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，将开发一个新的与健康相关的联邦学习和模型共享平台LEARNER，通过整合机器学习、可信人工智能和生物医学数据科学的跨学科专业知识，实现生物医学应用的协作大数据挖掘。学习者将结合基于严格理论基础的新型异步联邦学习算法，使用可信赖的人工智能技术、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异构健康数据之间的复杂关系。该项目将解决在生物医学和健康领域利用大数据的关键挑战，包括访问大型数据集、人工智能/机器学习算法的计算强度、超参数调整的复杂性，以及对有效的多学科专业知识和协作的需求。数据隐私是另一个关键问题，因为健康数据本质上是敏感的，即使数据经过仔细匿名处理，也可能被利用来暴露个人身份。LEARNER将包括一套协作数据分析和隐私保护机制和工具，这些机制和工具将安全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，并且诱导的结果可能难以解释，难以复制和再现。将采用新颖的方法来提高复杂健康数据分析模型的可解释性和可重复性。该项目团队将与来自学术界和工业界的个人一起，为研究生和本科生的培训和教育制定一个跨学科的计划。此外，还将为来自计算机科学与工程、信息学、电子工程、生物医学工程、生物学和统计学等不同专业的研究生和大四本科生开设一门跨学科的健康数据科学课程。该项目将特别强调吸引女性和少数族裔学生在学习者平台的背景下探索先进的计算技术。感兴趣的大四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项目PI团队合作。该项目也可作为理工科本科生的暑期项目进行。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

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-

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

Detached Error Feedback for Distributed SGD with Random Sparsification

• 发表时间: 2020-04
• 作者: An Xu;Heng Huang