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cs.LG2024
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…
cs.LG2023
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…