DP-CL(Continual Learning with Differential Privacy)

This is the official implementation of the Continual Learning with Differential Privacy.

If you use this code or our results in your research, please cite as appropriate:

@article{desai2021continual,
  title={Continual Learning with Differential Privacy},
  author={Pradnya, Desai and Lai, Phung and Phan, NhatHai and Thai, My},
  journal={International Conference on Neural Information Processing},
  year={2021}
}

Software Requirements

Python 3.7 is used for the current codebase.

Tensorflow 2.5

Experiments

The repository comes with instructions to reproduce the results in the paper or to train the model from scratch:

To reproduce the results:

• Clone or download the folder from this repository.

• Please find dataset on Google Drive folder.

• Go to folder DP-CL/ and Run ./replicate_results_xx.sh xx 3 where xx is the name of dataset and task that you'd like to run. For example: ./replicate_results_mnist.sh MNIST 3 for MNIST, ./replicate_results_cifar100.sh CIFAR 3 for CIFAR-100, ./replicate_results_cifar10.sh CIFAR 3 for CIFAR-10.

Potential issues

If you have any issues while running the code or further information, please send email directly to the first authors of this paper ([email protected] or [email protected]).