Manipulating vulnerability: Poisoning attacks and countermeasures in federated cloud-edge-client​ learning for image classification. - Yaru Zhao 002; Jianbiao Zhang; Yihao Cao (Knowl. Based Syst., 2023)

SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments. - Yihao Cao; Jianbiao Zhang; Yaru Zhao 002; Pengchong Su; Haoxiang Huang (Expert Syst. Appl., 2024)

FlexibleFL: Mitigating poisoning attacks with contributions in cloud-edge federated learning systems. - Yaru Zhao 002; Yihao Cao; Jianbiao Zhang; Haoxiang Huang; Yanhui Liu (Inf. Sci., 2024)

Privacy-Preserving Federated Learning With Improved Personalization and Poison Rectification of Client Models. - Yihao Cao; Jianbiao Zhang; Yaru Zhao 002; Hong Shen 001; Haoxiang Huang (IEEE Trans. Inf. Forensics Secur., 2024)

MOFDRNet: A Model for Data Leakage Attacks in Federated Learning. - Yaru Zhao 002; Jianbiao Zhang; Yihao Cao; Xianqun Han (Concurr. Comput. Pract. Exp., 2025)

Shielding federated learning: mitigating label transferability against poisoning attacks in cloud-edge-client system. - Yaru Zhao 002; Yihao Cao; Jianbiao Zhang; Zhaoqian Zhang; Weiru Wang 001 (Expert Syst. Appl., 2026)

ConfShield: A dual-stage fusion framework for robust and privacy-enhancing federated learning in cloud environments. - Yihao Cao; Jianbiao Zhang; Yaru Zhao 002; Detian Liu; Yufei Han; Weiwei Jiang 003; Tao Ye (Inf. Fusion, 2026)