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

Federated learning (FL) is an emerging privacy preserving machine learningprotocol that allows multiple devices to collaboratively train a shared globalmodel without revealing their private local data. Non-parametric models likegradient boosting decision trees (GBDT) have been commonly used in FL forvertically partitioned data. However, all these studies assume that all thedata labels are stored on only one client, which may be unrealistic forreal-world applications. Therefore, in this work, we propose a secure verticalFL framework, named PIVODL, to train GBDT with data labels distributed onmultiple devices. Both homomorphic encryption and differential privacy areadopted to prevent label information from being leaked through transmittedgradients and leaf values. Our experimental results show that both informationleakage and model performance degradation of the proposed PIVODL arenegligible.

PIVODL: Privacy-preserving vertical federated learning over distributed labels