CAREER: Towards Realizing Timely Information Transfer and Processing for Networked Communication Systems

职业：实现网络通信系统的及时信息传输和处理

• 批准号: 2146099
• 负责人: Ahmed Arafa
• 金额: $ 50万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146099&HistoricalAwards=false
• 关键词: CAREER; Towards; Realizing; Timely; Information

With the ever-growing amounts of data exchanged in today's world, there needs to be a reliable means for timely information processing and transfer between the end users. Inspired by the novel age-of-information (AoI) metric that assesses information freshness, this proposal offers a pathway towards realizing the ex- change of data in a timely manner in federated learning (FL), cloud computing, and remote sensing systems. It introduces a novel information timeliness metric, AoI, into the study of these systems, and investigates various approaches to achieve timely learning, computing and sensing over networks. It has the potential to become an enabler for various applications, including collaborative autonomous driving, precision health- care, and others. As an integral part of this research proposal, an extensive educational plan that involves K-12, undergraduate and graduate level agendas is proposed.This project spans three fundamental information processing categories. The first involves intelligent agents in a FL setup. Novel agent scheduling and efficient model compression algorithms are proposed for this category to realize the notion of timely FL at-scale. The second considers clients in cloud computing systems, for which innovative job uploading policies are devised under system-level constraints to achieve timely job completions. Finally, the third category focuses on remote sensing, and presents novel variable length coding schemes to enable timely multi-process tracking over noisy communication channels. This project has the potential to transform the way information acquisition and processing systems operate under stringent communication and resource constraints, while the educational plan provides the necessary tools to reason about the timeliness of information in networked systems.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.

随着当今世界交换的数据量不断增长，需要有一种可靠的手段来及时处理信息并在最终用户之间传输。受评估信息新鲜度的新型信息年龄（AoI）指标的启发，该提案提供了一条在联邦学习（FL）、云计算和遥感系统中及时实现数据交换的途径。它引入了一种新的信息及时性度量，AoI，到这些系统的研究，并研究各种方法来实现及时的学习，计算和传感网络。它有潜力成为各种应用的推动者，包括协作自动驾驶，精准医疗等。作为本研究计划的一个组成部分，提出了一个广泛的教育计划，涉及K-12，本科生和研究生水平的议程。第一个涉及FL设置中的智能代理。新的代理调度和高效的模型压缩算法，提出了这一类实现的概念，及时FL规模。第二个考虑客户端在云计算系统中，创新的工作上传政策的系统级的限制下，以实现及时的工作完成。最后，第三类侧重于遥感，并提出了新的可变长度编码方案，使及时多进程跟踪噪声通信信道。该项目有可能改变信息采集和处理系统在严格的通信和资源限制下的运行方式，而教育计划提供了必要的工具，以推理网络系统中信息的及时性。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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