A Multi-Rate Feedback Control Framework for Design and Analyzing of Decentralized and Federated Learning

用于设计和分析去中心化联邦学习的多速率反馈控制框架

• 批准号: 2311007
• 负责人: Mingyi Hong
• 金额: $ 47.2万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311007&HistoricalAwards=false
• 关键词: Multi; Rate; Feedback; Control; Framework

Distributed systems hold great promise to realize scalable processing and real-time intelligence required for our modern life, revolutionizing how we interact with technology and enabling us to easily tackle complex problems. However, despite extensive research in distributed algorithms and systems, several challenges persist in their synthesis and application. In particular, the current algorithm design process is not scalable. Indeed one needs to design a new algorithm and develop the corresponding analysis specific to each application scenario and set of requirements. There has been an urgent need to unify various subclasses of distributed algorithms to provide insights and streamline the design and analysis. The framework developed in this project will benefit a wide range of applications beyond machine learning, such as control theory and signal processing. The proposed activities also offer rich opportunities for engaging undergraduate students in cross-disciplinary research and K-12 outreach activities.This proposal advocates a generic “model” of distributed algorithms by leveraging theory and techniques from stochastic multi-rate feedback control systems. Such a generic model can abstract important features of distributed algorithms, such as guaranteed differential privacy, compressed communication, or occasional communication, into tractable modules. Building upon these abstract models, we design a framework with superior modeling power to encompass a substantial class of distributed algorithms. Such a framework can be used to analyze the entire algorithm class, but more importantly, it helps streamline the design of new algorithms in the sense that features arising from different application domains can be easily integrated.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.

分布式系统具有巨大的希望，可以实现现代生活所需的可扩展处理和实时智能，彻底改变了我们如何与技术互动，并使我们能够轻松解决复杂的问题。但是，在分布式算法和系统方面进行了广泛的研究，其合成和应用仍存在一些挑战。特别是，当前的算法设计过程是不可扩展的。确实，人们需要设计一种新算法并开发针对每个应用程序方案和一组要求的相应分析。迫切需要统一各种分布式算法的子类，以提供见解并简化设计和分析。该项目中开发的框架将使除了控制理论和信号处理之外，将受益于广泛的应用程序。拟议的活动还为使本科生参与跨学科研究和K-12外展活动提供了丰富的机会。该建议提倡通过利用随机多速率反馈控制系统的理论和技术来提倡分布式算法的通用“模型”。这样的通用模型可以抽象分布式算法的重要特征，例如保证的差异隐私，压缩通信或偶尔通信，成为可拖动的模块。在这些抽象模型的基础上，我们设计了一个具有出色建模能力的框架，以涵盖大量的分布式算法。这样的框架可用于分析整个算法类别，但更重要的是，它有助于简化新算法的设计，从某种意义上说，从不同的应用领域引起的特征可以很容易地集成。此奖项反映了NSF的法定任务，并通过评估该基金会的智力功能和广泛的影响来评估NSF的法定任务。