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

由于现代应用的数据量大，通信要求严格，需要及时地交换用于学习和计算目的的数据。本项目将用于评估网络时效性的信息时代（AoI）概念引入联邦学习（FL）的研究中，目的是为大规模联邦学习系统中的数据交换提供低延迟和通信效率的方法。该方案的重点是设计新的客户端调度、信息量化和客户端-服务器关联方法，以实现无线通信网络上的实时FL。这项研究预计将对使用FL的实时监控和信息共享系统的大规模部署产生重大而广泛的影响。它可能会影响各种应用，包括协作式自动驾驶、精确医疗保健等。所开发的算法、分析和实验将推动通信理论、网络和机器学习的最新技术，并自然地转化为pi在这些领域教授的本科和研究生课程。本计划的目标是设计和分析有效的代理调度策略和通信方案，以在施加各种系统级约束的通信网络上实现及时FL的概念。它包括三个主要推力。第一个重点是受AoI指标的启发，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，第二部分研究了新的联合模型压缩和调度方法，以提高不可靠网络下的通信效率，同时保持合理的FL性能。为了应对动态发展的通信环境，第三个重点是开发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平台对开发的算法进行彻底验证。该奖项反映了美国国家科学基金会的法定使命，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。

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.