  This article is in the context of gradient compression. Gradient compression
is a popular technique for mitigating the communication bottleneck observed
when training large machine learning models in a distributed manner using
gradient-based methods such as stochastic gradient descent. In this article,
assuming a Gaussian distribution for the components in gradient, we find the
rate distortion trade-off of gradient quantization schemes such as Scaled-sign
and Top-K, and compare with the Shannon rate distortion limit. A similar
comparison with vector quantizers also is presented.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence