Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth connections. A few methods have been proposed to compress gradient for efficient communication, but they either suffer a low compression ratio or significantly harm the resulting model accuracy, particularly when applied to convolutional neural networks. To address these issues, we propose a method to reduce the communication overhead of distributed deep learning. Our key observation is that gradient updates can be delayed until an unambiguous (high amplitude, low variance) gradient has been calculated. We also present an efficient algorithm to compute the variance with negligible additional cost. We experimentally show that our method can achieve very high compression ratio while maintaining the result model accuracy. We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.

本文提出了一种用于降低分布式深度学习通信开销的方法，通过延迟梯度更新以使得梯度可以在计算出高幅度、低方差的梯度后进行。在实验中，我们证明了该方法能够在保持模型准确性的同时实现非常高的压缩比，并分析了使用计算模型和通信模型的效率，提供了该方法能够实现在许多场景下使用基础设施的证据。

基于方差的梯度压缩用于高效的分布式深度学习