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: 2331711
• 负责人: Shuangqing Wei
• 金额: $ 25万
• 依托单位: Louisiana State 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=2331711&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.

技术进步使得大规模网络系统的部署成为可能，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目标。然而，由于大规模无线网络的吞吐量有限以及联网设备的计算能力有限，它们面临着挑战。该研究项目将开发一种新颖的通信高效的分层分布式优化框架，该框架集成了优化、通信和机器学习，以克服这些挑战。核心创新是对网络代理行为的模型进行采样和学习，然后使用这些模型来预测代理的响应，以实现明智的决策，同时最大限度地减少边缘不必要的通信和计算。由学习算法引导的自适应通信和优化算法被联合设计，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来进一步提高效率。为了实现这一目标，目标是开发（1）支持学习的分层分布式优化算法的通用框架； (2) 学习辅助的自适应量化、通信和查询方法； (3) 统一的机器学习框架，以实现通信节省和计算准确性之间的进一步权衡； (4) 通过现实应用对框架进行集成和验证。该项目的智力优势在于推进三个关键领域的知识——优化、通信和机器学习。将开发新的算法来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相结合，以满足较低的带宽需求。机器学习方法将得到改进，以解决通信和设备功能有限造成的不确定性。通过利用优化泛函结构将这三个领域紧密融合是该研究的一个值得注意的智力成果。这项研究的更广泛影响包括推进传感器网络、协作机器人、联邦学习和智能电网等现实世界的应用，这些应用支撑着现代社会工程基础设施。该项目将为本科生提供丰富的教育机会，通过跨两所大学和多个领域的综合项目获得研究经验。此外，研究结果将广泛公开，包括通过 YouTube 频道传播结果以及向高中生进行宣传。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。