6 citations · 12 across the 5 of their papers we have counts for
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cs.LG2022★ 2 cited
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning
Young Geun Kim, Carole-Jean Wu
Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collabora…
cs.LG2021
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
Young Geun Kim, Carole-Jean Wu
Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training sample…
cs.LG2020★ 2 cited
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance
Young Geun Kim, Carole-Jean Wu
Deep learning inference is increasingly run at the edge. As the programming and system stack support becomes mature, it enables acceleration opportunities within a mobile system, w…