Collaborative Research: An Integrated Framework for Learning-Enabled and Communication-Aware Hierarchical Distributed Optimization

协作研究：支持学习和通信感知的分层分布式优化的集成框架

• 批准号: 2331710
• 负责人: Truong Nghiem
• 金额: $ 25万
• 依托单位: Northern Arizona University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331710&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; Framework; Learning

Technological advancements have enabled the deployment of large-scale networked systems, such as sensor networks, robotic teams, and smart power grids. These systems aim to collaboratively optimize a shared objective. However, they face challenges due to limited throughput across large-scale wireless networks and restricted computational capabilities at networked devices. This research project will develop a novel communication-efficient hierarchical distributed optimization framework that integrates optimization, communication, and machine learning to overcome these challenges. The core innovation is to sample and learn models of the networked agents' behaviors, then use these models to predict the agents' responses to enable informed decision-making while minimizing unnecessary communication and computation at the edge. Adaptive communication and optimization algorithms guided by the learning algorithms are jointly designed to balance the trade-off between cost and accuracy, while exploiting correlation relationships among agents to further enhance efficiency. Towards this goal, the objectives are to develop (1) a general framework for learning-enabled hierarchical distributed optimization algorithms; (2) methods for learning-assisted adaptive quantization, communication, and query; (3) a unifying machine learning framework to attain further tradeoff between communication savings and computational accuracy; and (4) integration and validation of the framework through real-life applications.The intellectual merit of the project lies in advancing knowledge across three key areas - optimization, communication, and machine learning. New algorithms will be developed for collaborative hierarchical optimization across networked agents. Adaptive communication techniques will be integrated with predictive models to cater lower bandwidth needs. Machine learning methods will be improved to account for uncertainties caused by limited communication and device capabilities. Cohesive fusion of these three areas via exploiting structures of optimization functionals is a notable intellectual outcome of the research.The broader impacts of this research include advancing real-world applications like sensor networks, cooperative robotics, federated learning, and smart grids, which underpin modern engineering infrastructures. The project will provide an enriching education opportunity for undergraduate students to gain research experience through a comprehensive program across two universities and multiple fields. Furthermore, the findings will be publicized for broad accessibility, including dissemination of results through a YouTube channel and outreach to high school students.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.

技术进步已使大型网络系统的部署，例如传感器网络，机器人团队和智能电网。这些系统旨在协作优化共同的目标。但是，由于大规模无线网络的吞吐量有限，并且在网络设备上有限的计算功能，他们面临挑战。该研究项目将开发一个新型的沟通效率层次分布式优化框架，该框架集成了优化，通信和机器学习以克服这些挑战。核心创新是要采样和学习网络代理行为的模型，然后使用这些模型来预测代理的响应，以实现明智的决策，同时最大程度地减少边缘的不必要的沟通和计算。以学习算法为指导的自适应沟通和优化算法是共同设计的，以平衡成本和准确性之间的权衡，同时利用代理之间的相关关系以进一步提高效率。为了实现这一目标，目标是开发（1）用于启用学习层次分布式优化算法的一般框架； （2）学习辅助自适应量化，通信和查询的方法； （3）一个统一的机器学习框架，以在沟通节省和计算准确性之间取得进一步的权衡； （4）通过现实生活应用程序整合和验证框架。项目的智力优点在于跨三个关键领域的知识 - 优化，通信和机器学习。将开发新的算法，以跨网络代理进行协作层次优化。自适应通信技术将与预测模型集成，以满足较低的带宽需求。机器学习方法将得到改进，以说明由有限的通信和设备功能引起的不确定性。通过利用优化功能的结构，这三个领域的凝聚力融合是研究的显着智力结果。这项研究的更广泛的影响包括推进真实世界应用，例如传感器网络，合作机器人技术，联合的学习和智能网格，这是现代工程基础设施的基础。该项目将为本科生提供丰富的教育机会，可以通过两所大学和多个领域的全面计划获得研究经验。此外，这些调查结果将被公开以广泛的可访问性，包括通过YouTube渠道传播结果并向高中生推广。该奖项反映了NSF的法定任务，并被认为是值得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。