CAREER: A Unified Theory of Private Control Systems

职业：私人控制系统的统一理论

• 批准号: 1943275
• 负责人: Matthew Hale
• 金额: $ 50万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943275&HistoricalAwards=false
• 关键词: CAREER; Unified; Theory; Private; Control

The recent advances in artificial intelligence and wireless sensor technologies have led to significant research in cooperative optimization. In this regime, multiple agents (e.g., processors or sensors) communicate their information locally with their neighbors to cooperatively optimize a global performance metric. This decentralized paradigm plays a key role in the network domains where communication with a centralized coordinator is either undesirable or impossible. This also allows for preserving the privacy of the agents. It is for these reasons that the design and performance analysis of decentralized optimization methods have attracted a growing attention in several application domains such as data science, wireless networks, and communication networks. This project is aimed at development of new models, mathematical tools, and computational algorithms to address emerging complex multi-agent systems. This complexity arises in emerging applications such as remote sensing, economic dispatch models with renewable energy, and efficiency estimation in transportation networks. This proposal has the potential to substantially reduce the gap between the theory and real-world practice of complex multi-agent networks. Moreover, collaborations with the industrial partner will facilitate effective knowledge transfer. This project is also aimed at increasing awareness and interest among high school students, educators, and college students through several fully integrated educational and outreach activities. These include enhancing professional development of teachers of Stillwater High School, engaging secondary students in after school activities, and promoting diversity through involvement of underrepresented undergraduate students in research. The long-term research goal is to advance the computational models and algorithms for distributed constrained optimization in emerging complex multi-agent networks. In pursuit of this goal, the research objective of this CAREER proposal is to apply the theory of variational inequalities and regularization in the field of distributed optimization to design new algorithms with provable performance guarantees that can address multi-agent networks with complex constraints. This complexity arises in several application domains such as wireless sensor networks, transportation networks, and machine learning, where the optimization model is complicated due to the presence of: (1) uncertainty and nonlinearity in constraints; (2) an inner-level large-scale optimization problem; or (3) equilibrium constraints. The state-of-the-art approaches including weighted-averaging consensus, push-sum, and alternate direction multiplier methods work often under the premise that functional constraints are easy-to-project. These schemes rely significantly on Lagrangian duality theory and do not lend themselves to asynchronous protocols and communication delays. Accordingly, this research is expected to advance the area of distributed optimization over complex networks by: (i) Development of an enhanced mathematical modeling framework by utilizing the theory of variational inequalities; (ii) Design and analysis of new classes of iteratively regularized consensus-based algorithms with explicit performance bounds to address the proposed modeling framework; and (iii) Explore novel ways to address nonsmoothness in the proposed modeling framework. The long-term educational goal is to broaden the participation of K-12 and college students (in particular women and underrepresented minorities in STEM) in the fields of Operations Research and Applied Mathematics. In pursuit of this goal, the educational objective of this CAREER proposal is to inspire and engage young minds, formal and informal educators, and undergraduate and graduate students in understanding the role of optimization in tomorrow’s practice. This includes the following activities: (i) provide four-week professional development workshops for secondary teachers; (ii) develop an after school STEM program for Stillwater High School students; (iii) involve underrepresented undergraduate students in the PI’s research in collaboration with The Oklahoma Louis Stokes Alliance for Minority Participation; and (iv) develop an undergraduate and an advanced doctoral course.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.

人工智能和无线传感器技术的最新进展导致了合作优化的重要研究。在这种情况下，多个代理（例如，处理器或传感器）在本地与它们的邻居通信它们的信息，以协作地优化全局性能度量。这种分散的范例在网络域中起着关键作用，在这些网络域中，与集中式协调器的通信要么是不希望的，要么是不可能的。这也可以保护代理人的隐私。正是由于这些原因，分散优化方法的设计和性能分析在数据科学、无线网络和通信网络等多个应用领域引起了越来越多的关注。该项目旨在开发新的模型，数学工具和计算算法，以解决新兴的复杂多智能体系统。这种复杂性出现在新兴的应用中，如遥感，可再生能源的经济调度模型，以及运输网络的效率估计。这一建议有可能大大减少复杂的多智能体网络的理论和现实世界的实践之间的差距。此外，与工业伙伴的合作将促进有效的知识转移。该项目还旨在通过几项全面综合的教育和推广活动，提高高中生、教育工作者和大学生的认识和兴趣。这些措施包括加强静水高中教师的专业发展，从事课外活动的中学生，并通过参与研究的代表性不足的本科生促进多样性。长期的研究目标是在新兴的复杂多智能体网络中改进分布式约束优化的计算模型和算法。为了实现这一目标，本CAREER提案的研究目标是将变分不等式和正则化理论应用于分布式优化领域，以设计具有可证明性能保证的新算法，从而解决具有复杂约束的多智能体网络。这种复杂性出现在几个应用领域，如无线传感器网络，运输网络和机器学习，其中优化模型是复杂的，由于存在：（1）约束的不确定性和非线性;（2）内部级别的大规模优化问题;或（3）平衡约束。最先进的方法，包括加权平均共识，推和，交替方向乘数方法的工作往往是在功能性约束是易于项目的前提下。这些计划依赖于拉格朗日对偶理论，并不借给自己的异步协议和通信延迟。因此，这项研究预计将推进复杂网络上的分布式优化领域：（i）开发一个增强的数学建模框架，利用变分不等式理论;（ii）设计和分析新的类迭代正则化的共识为基础的算法，明确的性能界限，以解决所提出的建模框架;和（iii）探索新的方法来解决所提出的建模框架中的非平滑性。长期教育目标是扩大K-12和大学生（特别是妇女和STEM中代表性不足的少数民族）在运筹学和应用数学领域的参与。为了实现这一目标，本CAREER提案的教育目标是激励和吸引年轻人、正式和非正式教育工作者、本科生和研究生了解优化在未来实践中的作用。这包括以下活动：（一）为中学教师提供为期四周的专业发展讲习班;（二）为斯蒂尔沃特高中学生制定课后STEM计划;（三）与俄克拉荷马州路易斯斯托克斯少数民族参与联盟合作，让代表性不足的本科生参与PI的研究;以及（iv）开发本科生和高级博士课程。该奖项反映了NSF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

Differential Privacy in Cooperative Multiagent Planning

• DOI: 10.48550/arxiv.2301.08811
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Bo Chen;C. Hawkins;Mustafa O. Karabag;Cyrus Neary;M. Hale;U. Topcu

Differential Privacy for Stochastic Matrices Using the Matrix Dirichlet Mechanism

• DOI: 10.1109/cdc49753.2023.10383376
• 发表时间: 2023-12
• 期刊: 2023 62nd IEEE Conference on Decision and Control (CDC)
• 作者: Brandon Fallin;C. Hawkins;Bo Chen;Parham Gohari;Alexander Benvenuti;U. Topcu;Matthew T. Hale

Edge Differential Privacy for Algebraic Connectivity of Graphs

• DOI: 10.1109/cdc45484.2021.9683306
• 发表时间: 2021-04
• 期刊: 2021 60th IEEE Conference on Decision and Control (CDC)
• 作者: Bo Chen;C. Hawkins;Kasra Yazdani;M. Hale

Differentially Private LQ Control

• DOI: 10.1109/tac.2022.3148710
• 发表时间: 2018-07
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Kasra Yazdani;Austin M. Jones;Kevin J. Leahy;M. Hale

Spectral Differential Privacy: Application to Smart Meter Data

• DOI: 10.1109/jiot.2021.3107770
• 发表时间: 2021-02
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Kendall Parker;M. Hale;P. Barooah