CAREER: Foundations of Collaborative Machine Learning

职业：协作机器学习的基础

• 批准号: 2239374
• 负责人: Mehrdad Mahdavi
• 金额: $ 60万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239374&HistoricalAwards=false
• 关键词: CAREER; Foundations; Collaborative; Machine; Learning

Recent advances in machine learning rely on collecting an enormous amount of data and learning immense models in a centralized cloud. However, the excessive storage and computational needs of centralized approaches, alongside regulatory challenges in sharing private data, put the utility of this paradigm in doubt. Collaborative machine learning is a recent alternative paradigm to tackle these issues by developing algorithms collaboratively without exchanging or centralizing the data. For example, different geographically distributed hospitals, each being in possession of limited patients’ data, may collaboratively develop predictive algorithms to improve diagnostics and treatment beyond what could be accomplished alone. Unlocking the full potential of collaborative learning strongly depends on the ability to encourage a large pool of individuals or corporations to share their private data and resources. Towards this aim, this CAREER award offers an intersectional approach to develop theoretically-grounded collaborative algorithms to facilitate learning optimally from fragmented, heterogeneous private data under resource constraints by jointly addressing various computational, statistical, systems, and game-theoretic challenges. By promoting a stable and fair ecosystem to benefit and retain all participants, without imposing stringent data and resource constraints, this project’s outcomes promise to make data-driven intelligent systems more effective, personalized, and robust in a myriad of application domains, such as personalized healthcare, precision agriculture, and education.To promote a healthy data and compute ecosystem and enable optimal use of distributed heterogeneous data under resource constraints, this project offers an intersectional approach to rigorously address computational, statistical, and game-theoretic challenges. On the practical side, it introduces a pluralistic learning paradigm and develops distributed algorithms that are cognizant of statistical heterogeneity and confined to learning models that meet available resources. On the statistical side, the project focuses on establishing generalization guarantees and understanding information-theoretic tradeoffs, providing an opportunity for synergistic advancements and insights. The project makes an intimate connection between collaborative learning and aggregative games and leverages developed theoretical and algorithmic investigations to answer questions related to equilibrium, incentivization, fairness, and stability to promote a healthy ecosystem. The intersectional and unified study the project proposes, creates essential connections, and fosters new transformative methods not developed by efforts within the individual disciplines. The research will be integrated with education through hosting workshops, mentoring students, and developing courses.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.

机器学习的最新进展依赖于在集中式云中收集大量数据和学习庞大的模型。然而，集中式方法的过度存储和计算需求，以及共享私有数据的监管挑战，使这种模式的实用性受到质疑。协作机器学习是最近的一种替代范式，通过协作开发算法来解决这些问题，而无需交换或集中数据。例如，不同地理分布的医院（每个医院拥有有限的患者数据）可以协作地开发预测算法，以改进诊断和治疗，而不是单独完成。充分发挥协作学习的潜力，在很大程度上取决于鼓励大量个人或公司共享其私人数据和资源的能力。为了实现这一目标，这个CAREER奖提供了一种交叉的方法来开发理论基础的协作算法，以促进在资源限制下从碎片化，异构的私人数据中进行最佳学习，共同解决各种计算，统计，系统和博弈论挑战。通过促进一个稳定和公平的生态系统，使所有参与者受益并留住所有参与者，而不施加严格的数据和资源限制，该项目的成果有望使数据驱动的智能系统在无数应用领域更加有效，个性化和强大，例如个性化医疗保健，精准农业，和教育。为促进健康的数据和计算生态系统，并在资源有限的情况下优化使用分布式异构数据，这个项目提供了一个交叉的方法来严格解决计算，统计和博弈论的挑战。在实践方面，它引入了一个多元化的学习范式，并开发分布式算法，认识到统计异质性和局限于学习模型，满足可用资源。在统计方面，该项目侧重于建立泛化保证和理解信息理论权衡，为协同进步和见解提供机会。该项目将协作学习和聚合游戏紧密联系在一起，并利用先进的理论和算法研究来回答与平衡，激励，公平和稳定相关的问题，以促进健康的生态系统。该项目提出的交叉和统一的研究，创造了必要的联系，并促进了新的变革方法，而不是通过单个学科的努力来开发。该研究将通过举办研讨会、指导学生和开发课程与教育相结合。该奖项反映了NSF的法定使命，并被认为值得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来支持。