Skip to content
[NeurIPS 2019] Deep Leakage From Gradients
Python
Branch: master
Clone or download

Latest commit

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
assets public release Dec 12, 2019
models
.gitignore public release Dec 12, 2019
LICENSE
README.md [minor] update README.md Feb 12, 2020
main.py
utils.py fixing minor bugs and typos so that the main runs Jan 15, 2020

README.md

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!

def deep_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] )

  for iters in range(300):
    def closure():
      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() \
        for dummy_g, origin_g in zip(dummy_grad, origin_grad))
      
      grad_diff.backward()
      return grad_diff
    
    optimizer.step(closure)
    
  return  dummy_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 Open In Colab 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.

You can’t perform that action at this time.