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.

由于数据集庞大以及现代应用程序严格的通信要求，需要及时完成用于学习和计算目的的数据交换。该项目将用于评估网络及时性的信息时代（AoI）的概念引入到联邦学习（FL）的研究中，旨在为大规模数据交换提供低延迟和通信高效的手段FL 系统。该提案的重点是设计新颖的客户端调度、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及时 FL。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。开发的算法、分析和实验将推动通信理论、网络和机器学习领域的发展，并自然地转化为 PI 在这些领域教授的本科生和研究生课程。该项目的目标是设计并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。第一个重点是受 AoI 指标的启发，开发各种及时、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颖的联合模型压缩和调度方法，以提高不可靠网络上的通信效率，同时保持合理的 FL 性能。为了应对动态变化的通信环境，第三个推动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，将使用真实世界数据集和实验室测试台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价值和更广泛的影响审查标准进行评估，被认为值得支持。

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