![Federated Learning Is Not Private for Google GBoard Next Word Prediction [pdf]](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[Submitted on 30 Oct 2022 (
[v1](https://arxiv.org/abs/2210.16947v1)), last revised 9 Oct 2023 (this version, v2)]
[View PDF](https://arxiv.org/pdf/2210.16947)
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Abstract:In this paper we present new attacks against federated learning when used to train natural language text models. We illustrate the effectiveness of the attacks against the next word prediction model used in Google's GBoard app, a widely used mobile keyboard app that has been an early adopter of federated learning for production use. We demonstrate that the words a user types on their mobile handset, e.g. when sending text messages, can be recovered with high accuracy under a wide range of conditions and that counter-measures such a use of mini-batches and adding local noise are ineffective. We also show that the word order (and so the actual sentences typed) can be reconstructed with high fidelity. This raises obvious privacy concerns, particularly since GBoard is in production use.
Submission history #
From: Mohamed Suliman [
[view email](https://arxiv.org/show-email/2c4a8e2d/2210.16947)]
**Sun, 30 Oct 2022 20:58:34 UTC (1,254 KB)**
[\[v1\]](https://arxiv.org/abs/2210.16947v1)
**[v2]** Mon, 9 Oct 2023 21:05:32 UTC (1,483 KB)
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