Differentially Private Follow-the-Regularized-Leader (DP-FTRL)

Code for "Practical and Private (Deep) Learning without Sampling or Shuffling" by Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu. The paper proposed Differentially Private Follow-the-Regularized-Leader (DP-FTRL), a differentially private algorithm that does not rely on shuffling or subsampling as in Differentially Private Stochastic Gradient Descent (DP-SGD), but achieves comparable (or even better) utility.

This repository contains the implementation and experiments for centralized learning. Please see another repository for the implementation and experiments in the Federated learning setting.

This is not an officially supported Google product.

Overview of the code

The code is written in PyTorch.

main.py contains the training and evaluation steps for three datasets: MNIST, CIFAR10, and EMNIST (byMerge).
optimizers.py contains the DP-FTRL optimizer, and ftrl_noise.py contains the tree-aggregation protocol, which is the core of the optimizer.
privacy.py contains the privacy accounting function for DP-FTRL. This is for the variant where the data order is given and we use the binary tree completion trick (Appendix D.2.1 and D.3 in the paper). For the other privacy computations, please refer to the code in Tensorflow privacy library. (There were bugs in the previous version of the privacy accounting code. Please refer to the errata in the paper for details.)

Example usage of the code

First, install the packages needed. The code is implemented using PyTorch, with the Opacus library used for gradient clipping (but not noise addition).

# This is an example for creating a virtual environment. 
sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3.7 --system-site-packages env3
. env3/bin/activate

# Install the packages.
pip install -r requirements.txt

Then, we set up a path where the data will be downloaded.

export ML_DATA="path to where you want the datasets saved"  # set a path to store data

Now we can run the code to do DP-FTRL training. For example, the following command trains a small CNN for CIFAR-10 with DP-FTRL noise 46.3, batch size 500 for 100 epochs (restarting every 20 epochs).

run=1
CUDA_VISIBLE_DEVICES=0 PYTHONHASHSEED=$(($run - 1)) python main.py \
    --data=cifar10 --run=$run --dp_ftrl=true \
    --epochs=100 --batch_size=500 --noise_multiplier=46.3 \
    --restart=20 --effi_noise=True --tree_completion=True \
    --learning_rate=50 --momentum=0.9

The results will be written as a tensorboard file in the current folder (can be configured with flag dir). You can view it with tensorboard

tensorboard --port 6006 --logdir .

To get the privacy guarantee, please refer to the function compute_epsilon_tree in privacy.py. There is an example in the main of privacy.py for how to use it.

Citing this work

@article{kairouz2021practical,
  title={Practical and Private (Deep) Learning without Sampling or Shuffling},
  author={Kairouz, Peter and McMahan, H Brendan and Song, Shuang and Thakkar, Om and Thakurta, Abhradeep and Xu, Zheng},
  journal={arXiv preprint arXiv:2103.00039},
  year={2021}
}

ShikharJ/DP-FTRL

Differentially Private Follow-the-Regularized-Leader (DP-FTRL)

Overview of the code

Example usage of the code

Citing this work