权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Control theoretic approaches for dynamic and privacy preserving distributed optimization algorithms

职业：动态和隐私保护分布式优化算法的控制理论方法

基本信息

批准号：
1653838
负责人：
Solmaz Kia
金额：
$ 50万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-03-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1653838&HistoricalAwards=false
关键词：
CAREER Control theoretic approaches dynamic

项目摘要

Networked systems have undergone advances toward providing efficient solutions to many challenging problems in the modern world. Smart grid operations, smart transportation, and smart healthcare are just a few such networked operations that are envisioned to help us manage our resources efficiently using smarter interactions among their subsystems. However, optimally benefiting from these networked systems will require efficient operational algorithms, many of which involve in-network static or dynamic optimal decision-making. Effective solutions for such algorithms need to be distributed and implementable within the limitations inherent in communication devices. Furthermore, in many applications, these solutions also must have transparent privacy preservation properties to make them acceptable for everyday use. This proposal's research objective is to investigate how automatic control-theoretic tools can be used to develop and analyze distributed dynamic optimization algorithms that respect the restrictions inherent to communication channels and address concerns regarding privacy preservation of participating agents in smart network operation. The results of this research will facilitate the realization and adoption of optimal in-network solutions in many important cyber-physical applications, such as smart grid, sensor networks, and distributed data regression in the healthcare and banking sectors. This project research will be complemented by a multi-tiered educational, mentoring, and outreach plan to train and motivate the next generation of experts who will solve problems that will emerge along with the new paradigms of smart networked systems. The research will be integrated into the University of California, Irvine curriculum through three main avenues: (1) developing new graduate and undergraduate courses, (2) involving students in the PI's Cooperative Systems Lab, and (3) mentoring efforts at the college and high school levels.This project will significantly expand the current state of knowledge on distributed solutions for in-network dynamic optimization algorithm design in three separate directions: design techniques, efficient communication, and transparent privacy preservation characteristics. In terms of design techniques, the proposal will showcase a systematic distributed algorithm design using two time-scale dynamical system concepts from singular perturbation theory. For a robust, active, and smarter communication strategy, event-triggered communication approaches will be used to enable each agent to locally decide when it is necessary to communicate in order to preserve the algorithm's integrity. Finally, to achieve transparent privacy preservation, this project will include development of observability tests to identify a knowledge set that enables eavesdroppers to construct the private data of other agents in the network using sophisticated observers, such as neuro-observers. The project will also include design of solutions for resistance of these intrusions by developing tools and methods to create augmentations that induce privacy preservation in distributed dynamic optimization algorithms.

网络化系统已经朝着为现代世界中许多具有挑战性的问题提供有效解决方案的方向发展。智能电网运营、智能交通和智能医疗只是其中的几个联网运营，这些网络运营旨在帮助我们使用子系统之间更智能的交互来有效地管理我们的资源。然而，从这些网络化系统中获得最佳收益将需要高效的操作算法，其中许多算法涉及网络内静态或动态最佳决策。这种算法的有效解决方案需要在通信设备固有的限制内分布和实现。此外，在许多应用中，这些解决方案还必须具有透明的隐私保护特性，以使其能够被日常使用。该提案的研究目标是研究如何使用自动控制理论工具来开发和分析分布式动态优化算法，尊重通信渠道固有的限制，并解决有关参与智能网络操作的代理的隐私保护问题。这项研究的结果将有助于在许多重要的网络物理应用中实现和采用最佳的网络解决方案，例如智能电网，传感器网络以及医疗保健和银行部门的分布式数据回归。该项目研究将通过多层次的教育，指导和推广计划来补充，以培训和激励下一代专家，他们将解决沿着智能网络系统的新范式出现的问题。该研究将通过三个主要途径纳入加州大学欧文分校的课程：（1）开发新的研究生和本科生课程，（2）让学生参与PI的合作系统实验室，以及（3）在大学和高中阶段的指导工作。该项目将大大扩展目前关于分布式解决方案的知识，网络动态优化算法设计有三个方向：设计技术、高效通信和透明隐私保护特性。在设计技术方面，该提案将展示一个系统的分布式算法设计，使用两个时间尺度的动力系统的概念，从奇异摄动理论。对于一个强大的，积极的，更聪明的通信策略，事件触发的通信方法将被用来使每个代理本地决定什么时候需要通信，以保持算法的完整性。最后，为了实现透明的隐私保护，该项目将包括开发可观察性测试，以确定一个知识集，使窃听者能够使用复杂的观察者（如神经观察者）构建网络中其他代理的私人数据。该项目还将包括通过开发工具和方法来设计抵抗这些入侵的解决方案，以创建在分布式动态优化算法中诱导隐私保护的增强。