6 citations · 11 across the 6 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.LG2023★ 1 cited
Towards Understanding Adversarial Transferability in Federated Learning
Yijiang Li, Ying Gao, Haohan Wang
We investigate a specific security risk in FL: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients but l…
cs.CV2023★ 1 cited
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Yijiang Li, Xinjiang Wang, Lihe Yang +3
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, w…
cs.CR2023★ 1 cited
Multi-metrics adaptively identifies backdoors in Federated learning
Siquan Huang, Yijiang Li, Chong Chen +2
The decentralized and privacy-preserving nature of federated learning (FL) makes it vulnerable to backdoor attacks aiming to manipulate the behavior of the resulting model on speci…