Jing Yang
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Timely Computing and Learning over Communication Networks

Overview

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.

Participants

  • PIs:
    Ahmed Arafa, Associate Professor, Department of ECE, UNC Charlotte
    
Jing Yang, Associate Professor, Department of ECE, University of Virginia

  • Graduate Research Assistants:
    Md Nurul Absar Siddiky, Abdulmoneam Ali (UNC Charlotte)
    Ruiquan Huang, Renpu Liu, Donghao Li, Fengyu Gao (University of Virginia)

Selected Publications

  • Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis
    R. Huang*, D. Li*, C. Shi, C. Shen and J. Yang, The 41st Conference on Uncertainty in Artificial Intelligence (UAI), July 2025. (* Equal contribution)

  • How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias
    R. Huang, Y. Liang and J. Yang, The 42nd International Conference on Machine Learning (ICML), July 2025.

  • Decision Feedback In-Context Symbol Detection over Block-Fading Channels
    L. Fan, J. Yang, and C. Shen, IEEE International Conference on Communication (ICC), June 2025.

  • Efficient Prompt Optimization Through the Lens of Best Arm Identification
    C. Shi, K. Yang, Z. Chen, J. Li, J. Yang and C. Shen, The 38th Conference on Neural Information Processing Systems (NeurIPS), December 2024.

  • Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
    C. Shi, K. Yang, J. Yang and C. Shen, The 38th Conference on Neural Information Processing Systems (NeurIPS), December 2024.

  • Average Reward Reinforcement Learning for Wireless Radio Resource Management
    K. Yang, J. Yang and C. Shen, The 58th Asilomar Conference on Signals, Systems and Computers, Oct. 2024. (Best Student Paper Award Finalist)

  • 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.

  • 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.

  • 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.

  • 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

  • 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.

  • 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.

  • 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.

  • 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)

Acknowledgement

This project is supported in part by the U.S. NSF under grant CNS 2114537 and CNS 2531789. 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.