Draft: Feature/opacus dp
Three different features are considered here:
1- PoC for local differential privacy integration with opacus for pytorch models: two notebooks containing two tutorials: pytorch-opacus-classifier-CIFAR10.ipynb and pytorch-opacus-MNIST.ipynb. In addition opacus has been added to the environnements.
2- Creation of a new training plan for training pytorch models that allows enabling DP with opacus through a model arguments (optional). The new training plan is TorchTrainingDPPlan from fedbiomed.common.torchnnDP. Three additional notebooks are provided to check compatibility with this new training plan with previous examples and tutorials with opacus: 101_getting-started-2.ipynb, pytorch-opacus-classifier-CIFAR10-2.ipynb and pytorch-opacus-MNIST-2.ipynb
3- Possibility to add any torchvision dataset to a node and decide a ratio for randomly sampling only a portion of the selected dataset to induce heterogeneity at the node level. The modified files are cli.py and data_manager.py. When option 2) default is selected the client should give:
- Name of the database (from torchvision.datasets): --> e.g. MNIST if he wants to add the mnist dataset
- Ratio of #name dataset in the current node (float, 0<ratio<=1): --> e.g. 0.5 if we want to add only a randomly subset containing the 50% of samples from the selected dataset
- #name will be added with tags
['##name','#dataset'][y/N]: y
The three features can be considered separately. In particular 3- will entail some modifications at the documentation level.