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 this paper we present new attacks against federated learning when used totrain natural language text models. We illustrate the effectiveness of theattacks against the next word prediction model used in Google's GBoard app, awidely used mobile keyboard app that has been an early adopter of federatedlearning for production use. We demonstrate that the words a user types ontheir mobile handset, e.g. when sending text messages, can be recovered withhigh accuracy under a wide range of conditions and that counter-measures such ause of mini-batches and adding local noise are ineffective. We also show thatthe word order (and so the actual sentences typed) can be reconstructed withhigh fidelity. This raises obvious privacy concerns, particularly since GBoardis in production use.

Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction