30 citations · 145 across the 22 of their papers we have counts for
6 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…
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation
Ming Hu, Peiheng Zhou, Zhihao Yue +6
As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy.…
FedMR: Fedreated Learning via Model Recombination
Ming Hu, Zhihao Yue, Zhiwei Ling +2
As a promising privacy-preserving machine learning method, Federated Learning (FL) enables global model training across clients without compromising their confidential local data.…
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…
FedCAT: Towards Accurate Federated Learning via Device Concatenation
Ming Hu, Tian Liu, Zhiwei Ling +2
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local…