Collaborative Research: PPoSS: Planning: Integrated Scalable Platform for Privacy-aware Collaborative Learning and Inference

协作研究：PPoSS：规划：用于隐私意识协作学习和推理的集成可扩展平台

• 批准号: 2028839
• 负责人: Jimeng Sun
• 金额: $ 5万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028839&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Integrated

Building scalable distributed heterogeneous systems of the future with easy-to-program software is broadly acknowledged to be a grand challenge. It is widely recognized that a major disruption is currently under way in the design of computer systems as processors strive to extend, and go beyond, the end-game of Moore’s Law. This disruption is manifest in new forms of heterogeneous and distributed processors and memories at all scales (on-chip, on-die, on-node, on-rack, on-cluster, and on-data-center), rendering scalability as a fundamental challenge at all levels. Healthcare analytics offers a unique opportunity to explore scalable system design for the 21st century because there has been a tectonic shift in the ability of medical institutions to capture and store medical data, and to even stream data in real time. This shift has already contributed to an ecosystem of Machine Learning (ML) models being trained for a variety of clinical tasks. A new distributed heterogeneous architecture is required to build systems that can develop and deploy ML models based on distributed healthcare data that must necessarily be accessed with privacy-preserving constraints. Further, the proposed architecture must be accompanied by a software framework that can address the needs of domain-specific data scientists to develop and augment ML models being deployed in their hospitals.This planning grant project is exploring the foundational principles necessary in building integrated scalable distributed systems of the future, so as to prepare for submitting a full proposal to the PPoSS program. It uses the domain of healthcare analytics to motivate and concretize the research agenda, but the principles developed in this research should be applicable to other application domains as well. The exploration focuses on demonstrating an integrated platform that spans multiple levels of distribution and heterogeneity of computation and storage, while also obeying important privacy constraints. While recent progress on the use of ML in healthcare applications has been encouraging, current approaches do not a) scale to the degrees of parallelism, heterogeneity, and distribution that will be required in future systems, or b) support the soft real-time responsiveness to streaming data that is needed in many clinical situations. The originality of this project can be seen in the integration of distribution, heterogeneity, and privacy considerations in a single unified software/hardware stack, which includes adaptive resource management that spans privacy-preserving federated continuous learning, automatic specialization of ML models at individual sites, and automatic selection of ML models best suited for specific clinical tasks that maximize accuracy subject to different latency and soft real-time constraints.This project’s end-to-end approach to develop foundational scalability principles will impact multiple areas of computer science through publications, tutorials and courses, thereby benefiting other researchers working on scalability challenges in future distributed heterogeneous systems. The use of healthcare analytics as a driving application has the potential to result in significant benefits to society, by demonstrating how knowledge distilled from multiple sources of data can be embodied in recommendation systems that can run onsite to provide time-critical decision support to physicians. As a further impact, the project will contribute to the training of Highly Qualified Personnel (HQP) at the intersection of Systems for ML and ML for Healthcare — two emerging inter-disciplinary communities that are currently growing independent of each other. Finally, this research will leverage existing activities at the PIs’ institutions that contribute to broadening participation of underrepresented groups in computing.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.

通过易于编程软件的建筑可扩展的分布式异构系统被广泛认为是一个巨大的挑战。人们普遍认识到，作为处理器努力扩展并超越摩尔定律的末端游戏，目前正在设计重大破坏。这种破坏以各种尺度的新形式的异质和分布式处理器以及记忆（在片上，片上，野外，节点，插座，群集和DATA中心）中的新形式表现出来，将可伸缩性作为各个级别的基本挑战。 Healthcare Analytics为探索21世纪的可扩展系统设计提供了独特的机会，因为医疗机构捕获和存储医疗数据的能力已经发生了构造转移，甚至可以实时流媒体数据。这种转变已经促进了机器学习的生态系统（ML）模型，该模型正在培训各种临床任务。需要一个新的分布式异构体系结构来构建可以基于分布式医疗保健数据开发和部署ML模型的系统，必须使用隐私的约束来访问这些模型。此外，所提出的体系结构必须伴随软件框架，该框架可以解决特定于域的数据科学家的需求，以开发和增强其医院部署的ML模型。该规划赠款项目正在探索在构建未来综合缩放分布式系统中所必需的基本原理，以便为提交完整的PPOSS计划做好准备。它使用医疗保健分析的领域融合并融合了研究议程，但是本研究中开发的原理也应适用于其他应用领域。该探索着重于展示一个集成平台，该平台涵盖了计算和存储的多个分布和异质性，同时还要遵守重要的隐私限制。尽管在医疗保健应用中使用ML的最新进展一直令人鼓舞，但当前的方法并未a）扩展到未来系统中需要的并行性，异质性和分布程度，或者b）支持在许多临床情况下对流媒体数据的软性实时响应。该项目的独创性可以在单个统一的软件/硬件堆栈中的分布，异质性和隐私考虑的整合中看到，其中包括适应性的资源管理，涵盖具有隐私的持续学习的隐私性，自动专业化，ML模型的自动专业化，以及在单个站点上的ML模型，以及最大程度地适用于特定的临床任务，以适用于特定的临床任务。开发基本可伸缩性原则的方法将通过出版物，教程和课程影响计算机科学的多个领域，从而使其他研究人员受益于未来分布式异质系统中从事可伸缩性挑战的研究人员。通过证明如何在推荐系统中体现出从多个数据源中提取的知识，将医疗保健分析用作驾驶应用程序有可能为社会带来重大利益，这些知识可以在现场运行现场以向医生提供时间关键的决策支持。影响力，该项目将有助于在医疗保健的ML和ML系统的交集中对高素质人员（HQP）进行培训，这两个新兴的跨学科跨学科社区目前正在彼此之间成长。最后，这项研究将利用PIS机构的现有活动，这些活动有助于扩大代表性不足的小组在计算中的参与。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。

项目成果

AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings

AID：主动蒸馏机在私人数据设置中利用预先训练的黑盒模型

• DOI: 10.1145/3442381.3449944
• 发表时间: 2021
• 期刊: The Web conference
• 作者: Hoang, Trong Nghia;Hong, Shenda;Xiao, Cao;Low, Bryan;Sun, Jimeng
• 通讯作者: Sun, Jimeng