A Double Residual Compression Algorithm for Efficient Distributed Learning

Li Yao
Li Yao
Yan Ming
Yan Ming

AISTATS, pp. 133-143, 2019.

Cited by: 13|Views17
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Abstract:

Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchronization between the master and worker nodes becomes the major obstacle for efficient learning as the number of workers and the dimension of the model increase...More

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