28 citations · 103 across the 19 of their papers we have counts for
7 papers · 1 filter
Personalized Federated Learning via Backbone Self-Distillation
Pengju Wang, Bochao Liu, Dan Zeng +2
In practical scenarios, federated learning frequently necessitates training personalized models for each client using heterogeneous data. This paper proposes a backbone self-distil…
Federated Learning with Label-Masking Distillation
Jianghu Lu, Shikun Li, Kexin Bao +3
Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In…
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
Bochao Liu, Jianghu Lu, Pengju Wang +4
Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical d…
Learning Privacy-Preserving Student Networks via Discriminative-Generative Distillation
Shiming Ge, Bochao Liu, Pengju Wang +2
While deep models have proved successful in learning rich knowledge from massive well-annotated data, they may pose a privacy leakage risk in practical deployment. It is necessary…
Coupled Confusion Correction: Learning from Crowds with Sparse Annotations
Hansong Zhang, Shikun Li, Dan Zeng +2
As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sou…
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm
Shikun Li, Xiaobo Xia, Hansong Zhang +2
Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative…
Trustable Co-label Learning from Multiple Noisy Annotators
Shikun Li, Tongliang Liu, Jiyong Tan +2
Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multipl…