50 citations · 105 across the 21 of their papers we have counts for
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cs.LG2023
MISA: Unveiling the Vulnerabilities in Split Federated Learning
Wei Wan, Yuxuan Ning, Shengshan Hu +4
\textit{Federated learning} (FL) and \textit{split learning} (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping…
cs.CR2023★ 26 cited
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
Wei Wan, Shengshan Hu, Minghui Li +4
\textit{Federated learning} (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to v…
cs.LG2023★ 23 cited
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
Yechao Zhang, Shengshan Hu, Leo Yu Zhang +5
Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different…