Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
Authors: Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif | Published: 2022-07-05
ポイズニング
Defending against the Label-flipping Attack in Federated Learning
Authors: Najeeb Moharram Jebreel, Josep Domingo-Ferrer, David Sánchez, Alberto Blanco-Justicia | Published: 2022-07-05
FL-Defender: Combating Targeted Attacks in Federated Learning
Authors: Najeeb Jebreel, Josep Domingo-Ferrer | Published: 2022-07-02
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences
Authors: Daryna Oliynyk, Rudolf Mayer, Andreas Rauber | Published: 2022-06-16 | Updated: 2023-06-06
Deep Leakage from Model in Federated Learning
Authors: Zihao Zhao, Mengen Luo, Wenbo Ding | Published: 2022-06-10
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Authors: Kai Yue, Richeng Jin, Chau-Wai Wong, Dror Baron, Huaiyu Dai | Published: 2022-06-08 | Updated: 2022-10-14
Group privacy for personalized federated learning
Authors: Filippo Galli, Sayan Biswas, Kangsoo Jung, Tommaso Cucinotta, Catuscia Palamidessi | Published: 2022-06-07 | Updated: 2022-09-04
Building Robust Ensembles via Margin Boosting
Authors: Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala | Published: 2022-06-07
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Authors: Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Zhongliang Yang, Yongfeng Huang, Xing Xie | Published: 2022-06-07 | Updated: 2022-10-31
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
Authors: Yuxuan Wan, Han Xu, Xiaorui Liu, Jie Ren, Wenqi Fan, Jiliang Tang | Published: 2022-06-01