Nov 2, 2016 · We first show that for d dimensional data with n clients, a naive stochastic binary rounding approach yields a mean squared error (MSE) of \ ...

A critical step in all of the above algorithms is to estimate the mean of a set of vectors as in Eq. (1). One of the main bottlenecks in distributed algorithms ...

Distributed mean estimation with limited communication. Pages 3329 - 3337 ... low communication cost, we study communication efficient algorithms for distributed ...

We first show that for d dimensional data with n clients, a naive stochastic rounding approach yields a mean squared error Theta(d/n). We then show by applying ...

The distributed mean estimation problem is studied in [34] and an communication-efficient compressing algorithm using constant number of bits is devised. In [35] ...

Distributed Mean Estimation with Limited Communication. A. Proof of Lemma 7. The equality follows from the symmetry in HD. To prove the upper bound, observe ...

Distributed Mean Estimation with Limited Communication · A. Suresh, Felix X. Yu, +1 author H. B. McMahan · Published in International Conference on… 2 November ...

Abstract. Mean estimation, also known as average consensus, is an important computational primitive in decentralized systems. When the average of large vec-.

The quantized observation is sent to the central machine and the central machine uses their average as the final estimate. The sample mean is the efficient ...

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