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

This paper studies multiparty learning, aiming to learn a model using theprivate data of different participants. Model reuse is a promising solution formultiparty learning, assuming that a local model has been trained for eachparty. Considering the potential sample selection bias among different parties,some heterogeneous model reuse approaches have been developed. However,although pre-trained local classifiers are utilized in these approaches, thecharacteristics of the local data are not well exploited. This motivates us toestimate the density of local data and design an auxiliary model together withthe local classifiers for reuse. To address the scenarios where some localmodels are not well pre-trained, we further design a multiparty cross-entropyloss for calibration. Upon existing works, we address a challenging problem ofheterogeneous model reuse from a decision theory perspective and take advantageof recent advances in density estimation. Experimental results on bothsynthetic and benchmark data demonstrate the superiority of the proposedmethod.

Improving Heterogeneous Model Reuse by Density Estimation