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

In typical scenarios where the Federated Learning (FL) framework applies, itis common for clients to have insufficient training data to produce an accuratemodel. Thus, models that provide not only point estimations, but also somenotion of confidence are beneficial. Gaussian Process (GP) is a powerfulBayesian model that comes with naturally well-calibrated variance estimations.However, it is challenging to learn a stand-alone global GP since merging localkernels leads to privacy leakage. To preserve privacy, previous works thatconsider federated GPs avoid learning a global model by focusing on thepersonalized setting or learning an ensemble of local models. We presentFederated Bayesian Neural Regression (FedBNR), an algorithm that learns ascalable stand-alone global federated GP that respects clients' privacy. Weincorporate deep kernel learning and random features for scalability bydefining a unifying random kernel. We show this random kernel can recover anystationary kernel and many non-stationary kernels. We then derive a principledapproach of learning a global predictive model as if all client data iscentralized. We also learn global kernels with knowledge distillation methodsfor non-identically and independently distributed (non-i.i.d.) clients.Experiments are conducted on real-world regression datasets and showstatistically significant improvements compared to other federated GP models.

Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process