HBMD-FL: Heterogeneous Federated Learning Algorithm Based on Blockchain and Model Distillation. - Ye Li 041; Jiale Zhang 001; Junwu Zhu; Wenjuan Li 001 (EISA, 2022)

Fairness-Aware Federated Learning Framework on Heterogeneous Data Distributions. - Ye Li 041; Jiale Zhang 001; Yanchao Zhao; Bing Chen 002; Shui Yu 001 (ICC, 2024)

Infighting in the Dark: Multi-Labels Backdoor Attack in Federated Learning. - Ye Li 041; Yanchao Zhao; Chengcheng Zhu; Jiale Zhang 001 (CoRR, 2024)

Blockfd: blockchain-based federated distillation against poisoning attacks. - Ye Li 041; Jiale Zhang 001; Junwu Zhu; Wenjuan Li 001 (Neural Comput. Appl., 2024)

Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning. - Ye Li 041; Yanchao Zhao; Chengcheng Zhu; Jiale Zhang 001 (CVPR, 2025)

It Takes Two: Accelerating Accurate Federated Learning through Pipelined Intra-Batch Data Sampling and Training. - Chenghao Nu; Zhe Zhang 043; Ye Li 041; Yanchao Zhao (ICPP, 2025)

SFFL: Self-aware fairness federated learning framework for heterogeneous data distributions. - Jiale Zhang 001; Ye Li 041; Di Wu 050; Yanchao Zhao; Shivakumara Palaiahnakote (Expert Syst. Appl., 2025)

BDFirewall: Towards Effective and Expeditiously Black-Box Backdoor Defense in MLaaS. - Ye Li 041; Chengcheng Zhu; Yanchao Zhao; Jiale Zhang 001 (CoRR, 2025)