most citedFedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CV2026

All Vehicles Can Lie: Efficient Adversarial Defense in Fully Untrusted-Vehicle Collaborative Perception via Pseudo-Random Bayesian Inference

Yi Yu, Libing Wu, Zhuangzhuang Zhang +3

Collaborative perception (CP) enables multiple vehicles to augment their individual perception capacities through the exchange of feature-level sensory data. However, this fusion m…

cs.AI2026

CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space

Bingyi Liu, Jinbo He, Haiyong Shi +5

Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI. However, efficiently modeling and optimiz…

cs.CR2025★ 2 cited

FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning

Xinhai Yan, Libing Wu, Zhuangzhuang Zhang +3

Federated Learning (FL) enables collaborative model training while preserving data privacy, but it is highly vulnerable to backdoor attacks. Most existing defense methods in FL hav…