29 citations · 52 across the 10 of their papers we have counts for
5 papers · 1 filter
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Jun Xia, Yi Zhang, Zhihao Yue +3
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial inte…
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Ming Hu, Zeke Xia, Zhihao Yue +4
As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AI…
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
Zhiwei Ling, Zhihao Yue, Jun Xia +3
Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distribu…
Model-Contrastive Learning for Backdoor Defense
Zhihao Yue, Jun Xia, Zhiwei Ling +4
Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Dee…
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation
Jun Xia, Ting Wang, Jiepin Ding +2
Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs).Although the state-of-th…