5 papers
FedSGT: Exact Federated Unlearning via Sequential Group-based Training
Bokang Zhang, Hong Guan, Hong kyu Lee +3
Federated Learning (FL) enables collaborative, privacy-preserving model training, but supporting the "Right to be Forgotten" is especially challenging because data influences the m…
Exposing Vulnerabilities in RL: A Novel Stealthy Backdoor Attack through Reward Poisoning
Bokang Zhang, Chaojun Lu, Jianhui Li +1
Reinforcement learning (RL) has achieved remarkable success across diverse domains, enabling autonomous systems to learn and adapt to dynamic environments by optimizing a reward fu…
Bilateral Differentially Private Vertical Federated Boosted Decision Trees
Bokang Zhang, Zhikun Zhang, Haodong Jiang +5
Federated learning is a distributed machine learning paradigm that enables collaborative training across multiple parties while ensuring data privacy. Gradient Boosting Decision Tr…
Online Poisoning Attack Against Reinforcement Learning under Black-box Environments
Jianhui Li, Bokang Zhang, Junfeng Wu
This paper proposes an online environment poisoning algorithm tailored for reinforcement learning agents operating in a black-box setting, where an adversary deliberately manipulat…
NeRF: Privacy-preserving Training Framework for NeRF
Bokang Zhang, Yanglin Zhang, Zhikun Zhang +3
Neural Radiance Fields (NeRF) have revolutionized 3D computer vision and graphics, facilitating novel view synthesis and influencing sectors like extended reality and e-commerce. H…