most citedThe Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation

11 citations · 11 across the 1 of their papers we have counts for

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1 paper

cs.LG2023★ 11 cited

The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation

Huancheng Chen, Johnny, Wang +1

Heterogeneity of data distributed across clients limits the performance of global models trained through federated learning, especially in the settings with highly imbalanced class…