11 citations · 17 across the 13 of their papers we have counts for
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cs.LG2023★ 1 cited
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
Huancheng Chen, Haris Vikalo
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precisi…
cs.LG2023★ 2 cited
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
Huancheng Chen, Haris Vikalo
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult. Particularly chall…
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…