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

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型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。其核心创新是对网络代理的行为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。由学习算法指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，以进一步提高效率。为了实现这一目标，我们的目标是：（1）开发一个通用的框架，用于学习使能的分层分布式优化算法;（2）学习辅助的自适应量化，通信和查询的方法;（3）一个统一的机器学习框架，以实现进一步的通信节省和计算精度之间的权衡;以及（4）通过实际应用集成和验证框架。该项目的智力价值在于推进三个关键领域的知识-优化，通信和机器学习。新的算法将开发跨网络代理的协作层次优化。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，以解决有限的通信和设备能力造成的不确定性。通过利用优化泛函的结构，这三个领域的内聚融合是这项研究的一个显着的智力成果。这项研究的更广泛的影响包括推进现实世界的应用，如传感器网络，协作机器人，联邦学习和智能电网，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，通过跨两所大学和多个领域的综合计划获得研究经验。此外，研究结果将被广泛宣传，包括通过YouTube频道传播结果和向高中生推广。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。

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.