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The idea of federated learning is to collaboratively train a neural network on a server. Each user receives the current weights of the network and in turns sends parameter updates (gradients) based on local data. This protocol has been designed not only to train neural networks data-efficiently, but also to provide privacy benefits for users, as their input data remains on device and only parameter gradients are shared. But how secure is sharing parameter gradients? Previous attacks have provided a false sense of security, by succeeding only in contrived settings - even for a single image. However, by exploiting a magnitude-invariant loss along with optimization strategies based on adversarial attacks, we show that is is actually possible to faithfully reconstruct images at high resolution from the knowledge of their parameter gradients, and demonstrate that such a break of privacy is possible even for trained deep networks. We analyze the effects of architecture as well as parameters on the difficulty of reconstructing an input image and prove that any input to a fully connected layer can be reconstructed analytically independent of the remaining architecture. Finally we discuss settings encountered in practice and show that even aggregating gradients over several iterations or several images does not guarantee the user's privacy in federated learning applications.
Author Information
Jonas Geiping (University of Siegen)
Hello, I’m Jonas . I conduct research in computer science as postdoc at the University of Maryland. My background is in Mathematics, more specifically in mathematical optimization and I am interested in research that intersects current deep learning and mathematical optimization, with my main area of applications being computer vision.
Hartmut Bauermeister (University of Siegen)
Hannah Dröge (University of Siegen)
Michael Moeller (University of Siegen)
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