28 citations · 56 across the 18 of their papers we have counts for
4 papers · 2 filters
On the Learnability of Out-of-distribution Detection
Zhen Fang, Yixuan Li, Feng Liu +2
Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studi…
FedImpro: Measuring and Improving Client Update in Federated Learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi +4
Federated Learning (FL) models often experience client drift caused by heterogeneous data, where the distribution of data differs across clients. To address this issue, advanced re…
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
Rong Dai, Yonggang Zhang, Ang Li +3
One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server mode…
Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang, Zhiqin Yang, Xinmei Tian +3
Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Adva…