Collaborative Research: CNS Core: Small: Timely Computing and Learning over Communication Networks

由于数据集的海量和现代应用对通信的严格要求，为学习和计算目的而进行的数据交换需要及时完成。本项目将用于评估网络时效性的信息年龄(AOI)的概念引入到联邦学习(FL)的研究中，旨在为大规模FL系统中的数据交换提供低延迟和通信高效的手段。该方案的重点是设计新颖的客户端调度、信息量化和客户-服务器关联方法，以在无线通信网络上实现及时的FL。这项研究预计将产生重大的、更广泛的影响，使大规模部署使用FL的实时监控和信息共享系统。它可能会影响各种应用，包括协作自动驾驶、精准医疗等。开发的算法、分析和实验将推动通信理论、网络和机器学习的发展，并自然转化为这些领域的PI教授的本科生和研究生课程。本项目的目标是设计和分析有效的代理调度策略和通信方案，以实现在通信网络上施加各种系统级约束的及时FL的概念。它包括三个主要推力。第一个重点是在AOI度量的启发下，开发各种及时和低延迟的代理调度策略，并分析它们的收敛性能。为了进一步提高通信效率，第二个重点研究了新的联合模型压缩和调度方法，以在保持合理的FL性能的同时提高不可靠网络的通信效率。为了应对动态变化的通信环境，第三个推力开发了基于在线学习的代理分组和模型聚合方法，以实现及时的分层FL，其中多个服务器通过分层多跳网络连接在一起。最后，将使用真实世界的数据集和实验室测试对开发的算法进行彻底验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Due to the large volume of datasets and the stringent communication requirements by modern applications, the exchange of data for learning and computing purposes needs to be done in a timely manner. This project introduces the notion of age of information (AoI), used to assess timeliness in networks, into the study of federated learning (FL), with the aim of providing low-latency and communication-efficient means for data exchange in large-scale FL systems. The proposal focuses on designing novel client scheduling, information quantization and client-server association methods to enable timely FL over wireless communication networks. This research is expected to result in significant broader impacts rendering large-scale deployment of real-time monitoring and information sharing systems using FL. It can potentially impact various applications, including collaborative autonomous driving, precision healthcare, and others. The algorithms, analysis, and experimentation developed will advance the state of the art in communication theory, networking, and machine learning, and would naturally translate into undergraduate and graduate courses taught by the PIs in these areas.The goal of this project is to design and analyze efficient agent scheduling policies and communication schemes that realize the notion of timely FL over communication networks imposing various system level constraints. It includes three principal thrusts. The first thrust focuses on developing various timely and low-latency agent scheduling policies, inspired by the AoI metric, and analyzing their convergence performances. To further improve the communication efficiency, the second thrust investigates novel joint model compression and scheduling approaches to enhance the communication efficiency over unreliable networks while maintaining reasonable FL performance. To cope with the dynamically evolving communication environment, the third thrust develops online learning based agent grouping and model aggregation approaches to enable timely hierarchical FL, where multiple servers are connected together through a hierarchical multihop network. Finally, a thorough validation of the developed algorithms will be performed using real-world datasets and a lab testbed.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.