11 citations · 34 across the 18 of their papers we have counts for
3 papers · 1 filter
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning
Wenkai Yang, Deli Chen, Hao Zhou +3
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manne…
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Wenkai Yang, Yankai Lin, Guangxiang Zhao +3
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy. How…
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
Guangxiang Zhao, Wenkai Yang, Xuancheng Ren +3
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundar…