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

由于数据集数量庞大，现代应用对通信的要求严格，因此需要及时进行用于学习和计算目的的数据交换。该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，旨在为大规模FL系统中的数据交换提供低延迟和高效的通信手段。该建议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，使及时FL在无线通信网络。这项研究预计将产生更广泛的影响，从而大规模部署使用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。所开发的算法、分析和实验将推动通信理论、网络和机器学习的发展，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别的及时FL的概念约束它包括三个主要目标。第一个推力的重点是开发各种及时和低延迟的代理调度策略，AoI指标的启发，并分析其收敛性能。为了进一步提高通信效率，第二个推力研究新的联合模型压缩和调度方法，以提高不可靠网络的通信效率，同时保持合理的FL性能。为了科普动态变化的通信环境，第三个推力开发基于在线学习的代理分组和模型聚合方法，使及时分层FL，多个服务器连接在一起，通过分层多跳网络。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全面验证。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。

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.