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

Decentralized deep learning requires dealing with non-iid data acrossclients, which may also change over time due to temporal shifts. While non-iiddata has been extensively studied in distributed settings, temporal shifts havereceived no attention. To the best of our knowledge, we are first with tacklingthe novel and challenging problem of decentralized learning with non-iid anddynamic data. We propose a novel algorithm that can automatically discover andadapt to the evolving concepts in the network, without any prior knowledge orestimation of the number of concepts. We evaluate our algorithm on standardbenchmark datasets and demonstrate that it outperforms previous methods fordecentralized learning.

Concept-aware clustering for decentralized deep learning under temporal shift