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

To enhance the quality and speed of data processing and protect the privacyand security of the data, edge computing has been extensively applied tosupport data-intensive intelligent processing services at edge. Among thesedata-intensive services, ensemble learning-based services can in naturalleverage the distributed computation and storage resources at edge devices toachieve efficient data collection, processing, analysis.  Collaborative caching has been applied in edge computing to support servicesclose to the data source, in order to take the limited resources at edgedevices to support high-performance ensemble learning solutions. To achievethis goal, we propose an adaptive in-network collaborative caching scheme forensemble learning at edge. First, an efficient data representation structure isproposed to record cached data among different nodes. In addition, we design acollaboration scheme to facilitate edge nodes to cache valuable data for localensemble learning, by scheduling local caching according to a summarization ofdata representations from different edge nodes. Our extensive simulationsdemonstrate the high performance of the proposed collaborative caching scheme,which significantly reduces the learning latency and the transmission overhead.

Adaptive In-network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge