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

合作研究：CNS 核心：小型：通过通信网络进行及时计算和学习

• 批准号: 2114537
• 负责人: Ahmed Arafa
• 金额: $ 24.99万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114537&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

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.

由于数据集的大量数据和现代应用程序的严格通信要求，需要及时地进行学习和计算目的的数据。该项目介绍了信息时代（AOI）的概念（AOI），用于评估网络中的及时性，并在联合学习（FL）的研究中，目的是为大型FL系统中的数据交换提供低延迟和沟通效率的手段。该提案着重于设计新颖的客户调度，信息量化和客户服务器关联方法，以使无线通信网络及时地进行FL。预计这项研究将产生更广泛的影响，从而赋予使用FL的实时监控和信息共享系统的大规模部署。它可能会影响各种应用程序，包括协作自主驾驶，精密医疗保健等。开发的算法，分析和实验将推进交流理论，网络和机器学习中艺术的状态，并且自然会转化为PIS在这些领域中教授的本科和研究生课程。该项目的目的是设计和分析有效的代理计划的计划和沟通方案，这些方案和沟通范围均限制了各种系统层面的诺言。它包括三个主要推力。第一个推力重点是制定受AOI指标启发的各种及时和低延迟剂调度策略，并分析其收敛性能。为了进一步提高沟通效率，第二个推力研究了新型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。为了应对动态不断发展的通信环境，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，以启用及时的层次结构FL，其中多个服务器通过层次结构多ihop网络连接在一起。最后，将使用现实世界数据集和实验室测试台进行对开发算法的彻底验证。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准通过评估来获得支持的。

项目成果

Timely Multi-Process Estimation with Erasures

• DOI: 10.1109/ieeeconf56349.2022.10051950
• 发表时间: 2022-09
• 期刊: 2022 56th Asilomar Conference on Signals, Systems, and Computers
• 作者: Karim Banawan;A. Arafa;Karim G. Seddik

Heirarchical Federated Learning in Delay Sensitive Communication Networks

• DOI: 10.1109/ieeeconf56349.2022.10052096
• 发表时间: 2022-10
• 期刊: 2022 56th Asilomar Conference on Signals, Systems, and Computers
• 作者: Abdulmoneam Ali;A. Arafa