4 papers
PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning
Tao Liu, Jiguang Lv, Dapeng Man +7
Federated Learning (FL), as a popular distributed learning paradigm, has shown outstanding performance in improving computational efficiency and protecting data privacy, and is wid…
Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning
Xue Zhou, Dapeng Man, Chen Xu +6
Offline reinforcement learning (RL) heavily relies on the coverage of pre-collected data over the target policy's distribution. Existing studies aim to improve data-policy coverage…
Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learning
Tao Liu, Dapeng Man, Jiguang Lv +6
Federated learning (FL) is widely used in Internet-of-Things (IoT) systems, but its distributed training process also exposes it to backdoor attacks. Existing studies mainly consid…
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning
Tao Liu, Yuhang Zhang, Zhu Feng +4
Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimate…