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I have been reading this paper [1] and [2]. There is a statement that reads as follows:

The variance between compressed gradient and uncompressed gradient must be made as small as possible because this way it has been shown that the learning is accelerated.

I infer that in general the smaller the variance of gradient in SGD, the faster the convergence. How can we prove this statement?

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