1 code implementation • 5 Jan 2024 • Baijun Cheng, Shengming Zhao, Kailong Wang, Meizhen Wang, Guangdong Bai, Ruitao Feng, Yao Guo, Lei Ma, Haoyu Wang
Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years.
no code implementations • 13 Nov 2023 • Zirui Gong, Liyue Shen, Yanjun Zhang, Leo Yu Zhang, Jingwei Wang, Guangdong Bai, Yong Xiang
By equipping AGRAMPLIFIER with the existing Byzantine-robust mechanisms, we successfully enhance the model's robustness, maintaining its fidelity and improving overall efficiency.
no code implementations • 24 Jun 2022 • Mark Huasong Meng, Guangdong Bai, Sin Gee Teo, Zhe Hou, Yan Xiao, Yun Lin, Jin Song Dong
For those reasons, there is a high demand for trustworthy and rigorous methods to verify the robustness of neural network models.
1 code implementation • 2 Apr 2022 • Mark Huasong Meng, Guangdong Bai, Sin Gee Teo, Jin Song Dong
This may be unrealistic in practice, as the data controllers are often reluctant to provide their model consumers with the original data.
no code implementations • 27 Apr 2021 • Yanjun Zhang, Guangdong Bai, Xue Li, Surya Nepal, Ryan K L Ko
We prove that less information is exposed in CGD compared to that of traditional FL.
no code implementations • 23 May 2017 • Zhengkui Wang, Guangdong Bai, Soumyadeb Chowdhury, Quanqing Xu, Zhi Lin Seow
Social media platforms contain a great wealth of information which provides opportunities for us to explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing.