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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study adversary-resilient stochastic distributed optimization, in which$m$ machines can independently compute stochastic gradients, and cooperate tojointly optimize over their local objective functions. However, an$\alpha$-fraction of the machines are $\textit{Byzantine}$, in that they maybehave in arbitrary, adversarial ways. We consider a variant of this procedurein the challenging $\textit{non-convex}$ case. Our main result is a newalgorithm SafeguardSGD which can provably escape saddle points and findapproximate local minima of the non-convex objective. The algorithm is based ona new concentration filtering technique, and its sample and time complexitybounds match the best known theoretical bounds in the stochastic, distributedsetting when no Byzantine machines are present.  Our algorithm is very practical: it improves upon the performance of allprior methods when training deep neural networks, it is relatively lightweight,and it is the first method to withstand two recently-proposed Byzantineattacks.

Byzantine-Resilient Non-Convex Stochastic Gradient Descent