Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks
and Defenses

Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

8 December 2020

Cong Wang

Dusit Niyato

Papers citing "Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses"

6 / 6 papers shown

Title
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning Jieming Bian Zhu Fu Jie Xu FedML 21 13 0 15 Oct 2021
Poisoning the Unlabeled Dataset of Semi-Supervised Learning Nicholas Carlini AAML 144 68 0 04 May 2021
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping Xiaoyu Cao Minghong Fang Jia Liu Neil Zhenqiang Gong FedML 108 611 0 27 Dec 2020
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs Binghui Wang Ang Li H. Li Yiran Chen 82 115 0 08 Dec 2020
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models Zhengming Zhang Yaoqing Yang Z. Yao Yujun Yan Joseph E. Gonzalez Michael W. Mahoney FedML 31 36 0 26 Aug 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,032 0 29 Nov 2018