Timely Computing and Learning over Communication Networks

This page is dedicated for updates on the NSF funded project ‘‘Collaborative Research: CNS Core: Timely Computing and Learning over Communication Networks’’.

Personnel

UNC Charlotte

• PI: Ahmed Arafa (PI), Assistant Professor, Department of ECE, UNC Charlotte
  

• Graduate Research Assistants: Md Nurul Absar Siddiky, Abdulmoneam Ali

Penn State University

• PI: Jing Yang, Associate Professor, School of EECS, PSU

• Graduate Research Assistants: Ruiquan Huang, Renpu Liu

Award Information

This project is supported by the National Science Foundation (NSF) under Grants CNS 2114537 and CNS 2114542 from 10-1-22 until 09-30-24 (estimated). Awarded amount is $249,929.00 (Charlotte part) and $250,000.00 (Penn State part).

Summary of Project Goals and Outcomes

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.

Selected Publications

1. Random Orthogonalization for Federated Learning in Massive MIMO Systems
   X. Wei, C. Shen, J. Yang, and H. V. Poor
   IEEE Transactions on Wireless Communications, August 2023.

2. Federated Linear Contextual Bandits with User-level Differential Privacy
   R. Huang, H. Zhang, L. Melis, M. Shen, M. Hejazinia and J. Yang
   International Conference on Machine Learning (ICML), July 2023.

3. Exploiting Feature Heterogeneity for Improved Generalization in Federated Multi-task Learning
   R. Liu, C. Shen, J. Yang
   IEEE International Symposium on Information Theory (ISIT), June 2023.

4. Private Status Updating with Erasures: A Case for Retransmission Without Resampling
   A. Arafa and K. Banawan
   IEEE International Conference on Communications (ICC), Rome, Italy, May 2023

5. Hierarchical Federated Learning in Delay Sensitive Communication Networks
   A. Ali and A. Arafa
   Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 2022.

6. Timely Multi-Process Estimation with Erasures
   K. Banawan, A. Arafa, and K. G. Seddik
   Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 2022.

7. Precoding and Scheduling for AoI Minimization in MIMO Broadcast Channels
   S. Feng and J. Yang
   IEEE Trans. on Information Theory, vol. 68, no. 8, pp. 5185 - 5202, August 2022.

8. On Federated Learning with Energy Harvesting Clients
   C. Shen, J. Yang, and J. Xu
   IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, May 2022. (Invited paper)

Broader Impacts

PI Yang co-organized an EECS girls camp themed ‘‘design your own reality’’ at Penn State in summer 2022. The summer camp is one-week long, and attracted about 20 middle school students, where the majority of the participants were female or from other under-represented groups. The summer camp greatly increased the interests of the participants in CS and EE.

Acknowledgement

This project is supported in part by the U.S. NSF under grants CNS 2114537 and CNS 2114542. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.