English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we consider the distributed mean estimation problem where theserver has access to some side information, e.g., its local computed meanestimation or the received information sent by the distributed clients at theprevious iterations. We propose a practical and efficient estimator based on anr-bit Wynzer-Ziv estimator proposed by Mayekar et al., which requires noprobabilistic assumption on the data. Unlike Mayekar's work which only utilizesside information at the server, our scheme jointly exploits the correlationbetween clients' data and server' s side information, and also between data ofdifferent clients. We derive an upper bound of the estimation error of theproposed estimator. Based on this upper bound, we provide two algorithms on howto choose input parameters for the estimator. Finally, parameter regions inwhich our estimator is better than the previous one are characterized.

Improved Communication Efficiency for Distributed Mean Estimation with Side Information