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/

Zendy Plus

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

Zendy Tools

Zendy Open

Statistical heterogeneity of data present at client devices in a federatedlearning (FL) system renders the training of a global model in such systemsdifficult. Particularly challenging are the settings where due to communicationresource constraints only a small fraction of clients can participate in anygiven round of FL. Recent approaches to training a global model in FL systemswith non-IID data have focused on developing client selection methods that aimto sample clients with more informative updates of the model. However, existingclient selection techniques either introduce significant computation overheador perform well only in the scenarios where clients have data with similarheterogeneity profiles. In this paper, we propose HiCS-FL (Federated Learningvia Hierarchical Clustered Sampling), a novel client selection method in whichthe server estimates statistical heterogeneity of a client's data using theclient's update of the network's output layer and relies on this information tocluster and sample the clients. We analyze the ability of the proposedtechniques to compare heterogeneity of different datasets, and characterizeconvergence of the training process that deploys the introduced clientselection method. Extensive experimental results demonstrate that in non-IIDsettings HiCS-FL achieves faster convergence than state-of-the-art FL clientselection schemes. Notably, HiCS-FL drastically reduces computation costcompared to existing selection schemes and is adaptable to differentheterogeneity scenarios.

Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning