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

This paper considers a general data-fitting problem over a networked system,in which many computing nodes are connected by an undirected graph. This kindof problem can find many real-world applications and has been studiedextensively in the literature. However, existing solutions either need acentral controller for information sharing or requires slot synchronizationamong different nodes, which increases the difficulty of practicalimplementations, especially for a very large and heterogeneous system.  As a contrast, in this paper, we treat the data-fitting problem over thenetwork as a stochastic programming problem with many constraints. By adaptingthe results in a recent paper, we design a fully distributed and asynchronizedstochastic gradient descent (SGD) algorithm. We show that our algorithm canachieve global optimality and consensus asymptotically by only localcomputations and communications. Additionally, we provide a sharp lower boundfor the convergence speed in the regular graph case. This result fits theintuition and provides guidance to design a `good' network topology to speed upthe convergence. Also, the merit of our design is validated by experiments onboth synthetic and real-world datasets.

Fully Distributed and Asynchronized Stochastic Gradient Descent for Networked Systems