Year 2025

1. [Computer Network] Accelerating Personalized Federated Learning via Dynamic Gradient Substitution
   and Client Selection
   Z. Zhan, W. Liu, X. Zhang, C. Tan, L. Xue, H. Tan, X. Chen, 2025.
2. [IWQoS] PASTA: Training Acceleration for Vertical Federated Learning via Adaptive Pipeline Parallelism
   T. Wu, W. Liu, H. Liang, Z. Zhan, J. Duan, C. Wu, J. Zuo, X. Chen, X. Zhang, 2025.
3. [ICDCS] TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction
   W. Liu, Z. Zhan, C. Joe-Wong, E. Ngai, J. Duan, D. Guo, X. Chen, X. Zhang, 2025.

Year 2024

1. [MSN] FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
   Z. Zhan, W. Zhao, Y. Li, W. Liu, X. Zhang, C. Tan, C. Wu, D. Guo, X. Chen, 2024.
2. [IWQoS] Adaptive Personalized Federated Learning for Non-IID Data with Continual Distribution Shift
   S. Chen, W. Liu, X. Zhang, H. Xu, W. Lin, X. Chen, 2024.
3. [ECAI] FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
   H. Liang, Z. Zhan, W. Liu, X. Zhang, C. Tan, X. Chen, 2024.

Year 2023

1. [TMC] DYNAMITE: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets
   W. Liu, X. Zhang, J. Duan, C. Joe-Wong, Z. Zhou, X. Chen, 2023.
2. [IWQoS] AdaCoOpt: Leverage the interplay of batch size and aggregation frequency for federated learning
   W. Liu, X. Zhang, J. Duan, C. Joe-Wong, Z. Zhou, X. Chen, 2023.
3. [INFOCOM WKSHPS] Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency
   W. Liu, X. Zhang, J. Duan, C. Joe-Wong, Z. Zhou, X. Chen, 2023.