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

Federated learning (FL) is an effective technique to directly involve edgedevices in machine learning training while preserving client privacy. However,the substantial communication overhead of FL makes training challenging whenedge devices have limited network bandwidth. Existing work to optimize FLbandwidth overlooks downstream transmission and does not account for FL clientsampling.  In this paper we propose GlueFL, a framework that incorporates new clientsampling and model compression algorithms to mitigate low download bandwidthsof FL clients. GlueFL prioritizes recently used clients and bounds the numberof changed positions in compression masks in each round. Across three popularFL datasets and three state-of-the-art strategies, GlueFL reduces downstreamclient bandwidth by 27% on average and reduces training time by 29% on average.

GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning