A Geometric Framework for Adversarial Vulnerability in Machine Learning
Authors: Brian Bell | Published: 2024-07-03
ポイズニング
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Authors: Rui Ye, Jingyi Chai, Xiangrui Liu, Yaodong Yang, Yanfeng Wang, Siheng Chen | Published: 2024-06-15
RMF: A Risk Measurement Framework for Machine Learning Models
Authors: Jan Schröder, Jakub Breier | Published: 2024-06-15
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
Authors: Xiaoting Lyu, Yufei Han, Wei Wang, Jingkai Liu, Yongsheng Zhu, Guangquan Xu, Jiqiang Liu, Xiangliang Zhang | Published: 2024-06-10
A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks
Authors: Hengzhu Liu, Ping Xiong, Tianqing Zhu, Philip S. Yu | Published: 2024-06-10
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
Authors: Zhibo Xing, Zijian Zhang, Zi'ang Zhang, Jiamou Liu, Liehuang Zhu, Giovanni Russello | Published: 2024-06-03
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
Authors: Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bo Li, Radha Poovendran | Published: 2024-05-31 | Updated: 2024-06-05
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics
Authors: Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi, Alexander Branch, Gregory Pottie | Published: 2024-05-28 | Updated: 2024-06-02
Revisit, Extend, and Enhance Hessian-Free Influence Functions
Authors: Ziao Yang, Han Yue, Jian Chen, Hongfu Liu | Published: 2024-05-25 | Updated: 2024-10-20
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities
Authors: Chi-Hua Wang, Guang Cheng | Published: 2024-05-24