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

Alternating direction method of multiplier (ADMM) is a powerful method tosolve decentralized convex optimization problems. In distributed settings, eachnode performs computation with its local data and the local results areexchanged among neighboring nodes in an iterative fashion. During thisiterative process the leakage of data privacy arises and can accumulatesignificantly over many iterations, making it difficult to balance theprivacy-accuracy tradeoff. We propose Recycled ADMM (R-ADMM), where a linearapproximation is applied to every even iteration, its solution directlycalculated using only results from the previous, odd iteration. It turns outthat under such a scheme, half of the updates incur no privacy loss and requiremuch less computation compared to the conventional ADMM. Moreover, R-ADMM canbe further modified (MR-ADMM) such that each node independently determines itsown penalty parameter over iterations. We obtain a sufficient condition for theconvergence of both algorithms and provide the privacy analysis based onobjective perturbation. It can be shown that the privacy-accuracy tradeoff canbe improved significantly compared with conventional ADMM.

Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms