DPFed: Toward Fair Personalized Federated Learning with Fast Convergence. - Jiang Wu 011; Xuezheng Liu; Jiahao Liu 001; Miao Hu 001; Di Wu 001 (MSN, 2022)

FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment. - Jiahao Liu 001; Jiang Wu 011; Jinyu Chen; Miao Hu 001; Yipeng Zhou; Di Wu 001 (IJCAI, 2023)

FedDWA: Personalized Federated Learning with Online Weight Adjustment. - Jiahao Liu 001; Jiang Wu 011; Jinyu Chen; Miao Hu 001; Yipeng Zhou; Di Wu 001 (CoRR, 2023)

FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees. - Jiahao Liu 001; Yipeng Zhou; Di Wu 001; Miao Hu 001; Mohsen Guizani; Quan Z. Sheng (ICML, 2024)

Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach. - Yipeng Zhou; Runze Wang; Jiahao Liu 001; Di Wu 001; Shui Yu 001; Yonggang Wen 001 (IEEE Trans. Dependable Secur. Comput., 2024)

Local Differentially Private Release of Infinite Streams With Temporal Relevance. - Runze Wang; Jiahao Liu 001; Miao Hu 001; Yipeng Zhou; Di Wu 001 (WWW, 2025)

CODP: Improving Differentially Private Federated Learning by Cascading and Offsetting Noises Between Iterations. - Runze Wang; Yipeng Zhou; Jiahao Liu 001; Xuezheng Liu; Miao Hu 001; Di Wu 001; Quan Z. Sheng; Song Guo 001 (IEEE Trans. Dependable Secur. Comput., 2025)