Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning
Authors: Joon Kim, Sejin Park | Published: 2024-08-15
Poisoning
FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method
Authors: Shihua Sun, Pragya Sharma, Kenechukwu Nwodo, Angelos Stavrou, Haining Wang | Published: 2024-08-13
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
Authors: Qilei Li, Ahmed M. Abdelmoniem | Published: 2024-08-05 | Updated: 2024-08-16
Model Hijacking Attack in Federated Learning
Authors: Zheng Li, Siyuan Wu, Ruichuan Chen, Paarijaat Aditya, Istemi Ekin Akkus, Manohar Vanga, Min Zhang, Hao Li, Yang Zhang | Published: 2024-08-04
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Authors: Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann | Published: 2024-07-15 | Updated: 2024-10-14
A Geometric Framework for Adversarial Vulnerability in Machine Learning
Authors: Brian Bell | Published: 2024-07-03
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