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

Distributed learning has become an integral tool for scaling up machinelearning and addressing the growing need for data privacy. Although more robustto the network topology, decentralized learning schemes have not gained thesame level of popularity as their centralized counterparts for being lesscompetitive performance-wise. In this work, we attribute this issue to the lackof synchronization among decentralized learning workers, showing bothempirically and theoretically that the convergence rate is tied to thesynchronization level among the workers. Such motivated, we present a noveldecentralized learning framework based on nonlinear gossiping (NGO), thatenjoys an appealing finite-time consensus property to achieve bettersynchronization. We provide a careful analysis of its convergence and discussits merits for modern distributed optimization applications, such as deepneural networks. Our analysis on how communication delay and randomized chatsaffect learning further enables the derivation of practical variants thataccommodate asynchronous and randomized communications. To validate theeffectiveness of our proposal, we benchmark NGO against competing solutionsthrough an extensive set of tests, with encouraging results reported.

Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping