34 citations · 38 across the 4 of their papers we have counts for
10 papers
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang +1
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus…
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia +1
Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malicious clients poison the training process via manipulating their local training d…
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients
Xiaoyu Cao, Neil Zhenqiang Gong
Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realis…
Provably Secure Federated Learning against Malicious Clients
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
Federated learning enables clients to collaboratively learn a shared global model without sharing their local training data with a cloud server. However, malicious clients can corr…
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
Jinyuan Jia, Xiaoyu Cao, Neil Zhenqiang Gong
In a \emph{data poisoning attack}, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. \emph{Bootstrap Aggregating (b…
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Binghui Wang, Xiaoyu Cao, Jinyuan jia +1
Backdoor attack is a severe security threat to deep neural networks (DNNs). We envision that, like adversarial examples, there will be a cat-and-mouse game for backdoor attacks, i.…