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2020 | OriginalPaper | Buchkapitel

Deep Leakage from Gradients

verfasst von : Ligeng Zhu, Song Han

Erschienen in: Federated Learning

Verlag: Springer International Publishing

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Abstract

Exchanging model updates is a widely used method in the modern federated learning system. For a long time, people believed that gradients are safe to share: i.e., the gradients are less informative than the training data. However, there is information hidden in the gradients. Moreover, it is even possible to reconstruct the private training data from the publicly shared gradients. This chapter discusses techniques that reveal information hidden in gradients and validate the effectiveness on common deep learning tasks. It is important to raise people’s awareness to rethink the gradient’s safety. Several possible defense strategies have also been discussed to prevent such privacy leakage.

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Titel: Deep Leakage from Gradients
verfasst von: Ligeng Zhu
Song Han
Verlag: Springer International Publishing
Buch: Federated Learning
Print ISBN: 978-3-030-63075-1

Electronic ISBN: 978-3-030-63076-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-63076-8_2

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