GDST: Global Distillation Self-Training for Semi-Supervised Federated Learning. - Xinyi Liu; Linghui Zhu; Shu-Tao Xia; Yong Jiang 001; Xue Yang 003 (GLOBECOM, 2021)

Defending against Model Stealing via Verifying Embedded External Features. - Yiming Li 004; Linghui Zhu; Xiaojun Jia; Yong Jiang 001; Shu-Tao Xia; Xiaochun Cao (AAAI, 2022)

Iterative Semi-Supervised Manifold Alignment For Hyperspectral Image Classification. - Linghui Zhu; Li Ma; Xingmei Li (WHISPERS, 2022)

MOVE: Effective and Harmless Ownership Verification via Embedded External Features. - Yiming Li 004; Linghui Zhu; Xiaojun Jia; Yang Bai; Yong Jiang 001; Shu-Tao Xia; Xiaochun Cao (CoRR, 2022)

A Fine-Grained Differentially Private Federated Learning Against Leakage From Gradients. - Linghui Zhu; Xinyi Liu; Yiming Li 004; Xue Yang 003; Shu-Tao Xia; Rongxing Lu (IEEE Internet Things J., 2022)

Not All Samples Are Born Equal: Towards Effective Clean-Label Backdoor Attacks. - Yinghua Gao; Yiming Li 004; Linghui Zhu; Dongxian Wu; Yong Jiang 001; Shu-Tao Xia (Pattern Recognit., 2023)

MOVE: Effective and Harmless Ownership Verification via Embedded External Features. - Yiming Li 004; Linghui Zhu; Xiaojun Jia; Yang Bai; Yong Jiang 001; Shu-Tao Xia; Xiaochun Cao; Kui Ren 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Holmes: Towards Effective and Harmless Model Ownership Verification to Personalized Large Vision Models via Decoupling Common Features. - Linghui Zhu; Yiming Li 004; Haiqin Weng; Yan Liu; Tianwei Zhang 004; Shu-Tao Xia; Zhi Wang 001 (CoRR, 2025)