PADL: Privacy-Aware and Asynchronous Deep Learning for IoT Applications. - Xiaoyuan Liu 002; Hongwei Li 001; Guowen Xu; Sen Liu 007; Zhe Liu 001; Rongxing Lu (IEEE Internet Things J., 2020)

Adaptive privacy-preserving federated learning. - Xiaoyuan Liu 002; Hongwei Li 001; Guowen Xu; Rongxing Lu; Miao He (Peer-to-Peer Netw. Appl., 2020)

Cross the Chasm: Scalable Privacy-Preserving Federated Learning against Poisoning Attack. - Yiran Li; Guiqiang Hu; Xiaoyuan Liu 002; Zuobin Ying (PST, 2021)

Privacy-Enhanced Federated Learning Against Poisoning Adversaries. - Xiaoyuan Liu 002; Hongwei Li 001; Guowen Xu; Zongqi Chen; Xiaoming Huang; Rongxing Lu (IEEE Trans. Inf. Forensics Secur., 2021)

Efficient Homomorphic Convolution for Secure Deep Learning Inference. - Xiaoyuan Liu 002; Hongwei Li 001; Qinyuan Qian; Hao Ren 001 (PST, 2023)

Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks. - Renyang Liu 001; Wei Zhou 011; Jinhong Zhang; Xiaoyuan Liu 002; Peiyuan Si; Haoran Li (SecureComm (1), 2023)

QPFFL: Advancing Federated Learning with Quantum-Resistance, Privacy, and Fairness. - Ran Zhang; Hongwei Li 001; Xinyuan Qian 002; Xiaoyuan Liu 002; Wenbo Jiang 001 (GLOBECOM, 2024)

Incentive and Dynamic Client Selection for Federated Unlearning. - Yijing Lin; Zhipeng Gao 001; Hongyang Du; Dusit Niyato; Jiawen Kang 001; Xiaoyuan Liu 002 (WWW, 2024)

DDFAD: Dataset Distillation Framework for Audio Data. - Wenbo Jiang 001; Rui Zhang 003; Hongwei Li 001; Xiaoyuan Liu 002; Haomiao Yang; Shui Yu 001 (CoRR, 2024)

Secure and Lightweight Feature Selection for Horizontal Federated Learning. - Xiaoyuan Liu 002; Hongwei Li 001; Guowen Xu; Xilin Zhang; Tianwei Zhang 004; Jianying Zhou 001 (IEEE Trans. Inf. Forensics Secur., 2025)