Yu-Can Zhou

Disentangled Embedding Optimization for Diffusion-Based Dataset Distillation

Release the Potential of Relative Feature Consistency for Data Heterogeneous Federated Learning

Federated Learning is a promising paradigm to achieve privacy-preserving collaborative training. However, its performance degrades severely under data heterogeneity. A main obstacle is the divergent local representations induced by data heterogeneity, which will degrade the quality of the aggregated global representati...

Release the Potential of Relative Feature Consistency for Data Heterogeneous Federated Learning

Federated Learning is a promising paradigm to achieve privacy-preserving collaborative training. However, its performance degrades severely under data heterogeneity. A main obstacle is the divergent local representations induced by data heterogeneity, which will degrade the quality of the aggregated global representati...