28 citations · 35 across the 3 of their papers we have counts for
3 papers
cs.CR2023★ 28 cited
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification
Zirui Gong, Liyue Shen, Yanjun Zhang +4
The collaborative nature of federated learning (FL) poses a major threat in the form of manipulation of local training data and local updates, known as the Byzantine poisoning atta…
cs.CR2023★ 1 cited
Bounded and Unbiased Composite Differential Privacy
Kai Zhang, Yanjun Zhang, Ruoxi Sun +5
The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, tradi…
cs.CR2023★ 6 cited
Client-side Gradient Inversion Against Federated Learning from Poisoning
Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang +5
Federated Learning (FL) enables distributed participants (e.g., mobile devices) to train a global model without sharing data directly to a central server. Recent studies have revea…