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) allows a number of agents to participate in traininga global machine learning model without disclosing locally stored data.Compared to traditional distributed learning, the heterogeneity (non-IID) ofthe agents slows down the convergence in FL. Furthermore, many datasets, beingtoo noisy or too small, are easily overfitted by complex models, such as deepneural networks. Here, we consider the problem of using FL regression on noisy,hierarchical and tabular datasets in which user distributions are significantlydifferent. Inspired by Latent Class Regression (LCR), we propose a novelprobabilistic model, Hierarchical Latent Class Regression (HLCR), and itsextension to Federated Learning, FEDHLCR. FEDHLCR consists of a mixture oflinear regression models, allowing better accuracy than simple linearregression, while at the same time maintaining its analytical properties andavoiding overfitting. Our inference algorithm, being derived from Bayesiantheory, provides strong convergence guarantees and good robustness tooverfitting. Experimental results show that FEDHLCR offers fast convergenceeven in non-IID datasets.

Federated Latent Class Regression for Hierarchical Data