This repository contains simulations for our private ensemble inference method.

• Install conda and torch manually (recommended)
• pip install -r requirements.txt

• First train and cache the device models.
• Then you can generate figures, tables or run raw experiments.

• python train.py --data <data_name> --num_repeats 10 --num_devices 20 --num_epochs 50
• <data_name> can be cifar10, cifar100, mnist, fashionmnist

• python nlp_train.py --data <data_name> --num_repeats 10 --num_devices 20
• <data_name> can be yelp_review_full, yelp_polarity, imdb, emotion

• See the bottom of ota_private_ensemble.py

• Run python figure_comparison_table.py

### Generate TeX Code for the Varying Conditions pgfplot

• Run python figure_conditions.py