Deep Leakage From Gradients [arXiv][Webside]

@inproceedings{zhu19deep,
title={Deep Leakage from Gradients},
author={Zhu, Ligeng and Liu, Zhijian and Han, Song},
booktitle={Advances in Neural Information Processing Systems},
year={2019}
}

Gradients exchaging is popular used in modern multi-node learning systems. People used to believe numerical gradients are safe to share. But we show that it is actually possible to obtain the training data from shared gradients and the leakage is pixel-wise accurate for images and token-wise matching for texts.

Overview

The core algorithm is to match the gradients between dummy data and real data.

It can be implemented in less than 20 lines with PyTorch!

defdeep_leakage_from_gradients(model, origin_grad): dummy_data=torch.randn(origin_data.size())
dummy_label=torch.randn(dummy_label.size())
optimizer=torch.optim.LBFGS([dummy_data, dummy_label] )
foritersinrange(300):
defclosure():
optimizer.zero_grad()
dummy_pred=model(dummy_data) dummy_loss=criterion(dummy_pred, F.softmax(dummy_label, dim=-1)) dummy_grad=grad(dummy_loss, model.parameters(), create_graph=True)
grad_diff=sum(((dummy_grad-origin_grad) **2).sum() \
fordummy_g, origin_ginzip(dummy_grad, origin_grad))
grad_diff.backward()
returngrad_diffoptimizer.step(closure)
returndummy_data, dummy_label

Prerequisites

To run the code, following libraies are required

• Python >= 3.6
• PyTorch >= 1.0
• torchvision >= 0.4

Code

Note: We provide for quick reproduction.

# Single image on CIFAR
python main.py --index 25
# Deep Leakage on your own Image
python main.py --image yours.jpg

Deep Leakage on Batched Images

Deep Leakage on Language Model

License

This repository is released under the MIT license. See LICENSE for additional details.