50 citations · 83 across the 7 of their papers we have counts for
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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.CR2022★ 50 cited
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection
Wei Wan, Shengshan Hu, Jianrong Lu +3
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine a…
cs.CR2021★ 3 cited
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning
Junyu Shi, Wei Wan, Shengshan Hu +2
Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained…