3 citations · 6 across the 4 of their papers we have counts for
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cs.DC2022★ 1 cited
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization
Heting Liu, Fang He, Guohong Cao
Federated learning (FL) enables geographically dispersed edge devices (i.e., clients) to learn a global model without sharing the local datasets, where each client performs gradien…
cs.LG2022★ 2 cited
Prediction of GPU Failures Under Deep Learning Workloads
Heting Liu, Zhichao Li, Cheng Tan +4
Graphics processing units (GPUs) are the de facto standard for processing deep learning (DL) tasks. Meanwhile, GPU failures, which are inevitable, cause severe consequences in DL t…