Controallble Machine Unlearning

Code instruction for Paper: Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning (WWW 2024) now available on arxiv: https://arxiv.org/abs/2310.18574 Camera ready version coming soon.

Introduction

Here is the Controllable Machine Unlearning (ConMU) repository. We conduct our experiments on CV benchmarks for both the random forgetting and class-wise forgetting.

Environment Download:

conda temp create -f conmu.yml

Random Forgetting

Here is the command to perform ConMU's random forgetting using ResNet-18 on CIFAR10 dataset.

python -u main.py --batch_size 128 --data CV_Data/cifar10 --dataset cifar10 --num_classes 10 --arch resnet18 --forget_percentage 0.2 
--save_dir ./dataset/resnet18 --epochs 100 --retrain_epoch 80 --lr 1e-3 --retrain_lr 1e-3 --prune_type rewind_lt --retain_filter_up 0.314 
--retain_filter_lower 0.314 --forget_filter_up 0.312 --forget_filter_lower 0.314 --num_noise 5 --further_train_lr 1e-2 --further_train_epoch 5 
--incompetent_epoch 3 --kl_weight 1.0

Parameters

• batch_size: batch size for the data
• data: location of the data corpus
• dataset: name of the dataset (cifar10, cifar100, svhn)
• num_classes: number of classes in the dataset
• arch: model architecture (Refer to cv_models/init.py).
• forget_percentage: percentage of data to forget
• save_dir: directory to save the model checkpoints
• epochs: number of epochs to train the original model
• retrain_epoch: number of epochs to train the gold model (retrained model using retained dataset)
• lr: learning rate for the original model
• retrain_lr: learning rate for the gold model
• num_noise: amount of noise (standard Gaussian) to add to the forgotten datasets
• further_train_lr: learning rate for the ConMU
• further_train_epoch: number of epochs to train the ConMU
• incompetent_epoch: number of epochs to train the unlearning proxy model

More details be be found at arg_parser.py.

Class-wise Forgetting

Here is the command to perform ConMU's class-wise forgetting using ResNet-18 on CIFAR10 dataset.

python -u main.py --batch_size 128 --data CV_Data/cifar10 --dataset cifar10 --num_classes 10 --arch resnet18 --forget_percentage 0.5 
--save_dir ./dataset/resnet18 --epochs 100 --retrain_epoch 80 --lr 1e-3 --retrain_lr 1e-3 --prune_type rewind_lt --retain_filter_up 0.314 
--retain_filter_lower 0.5 --forget_filter_up 0.5 --forget_filter_lower 0.5 --num_noise 5 --further_train_lr 1e-2 --further_train_epoch 5 
--incompetent_epoch 3 --kl_weight 0.5 --class_wise 5

Note that the the main differences between random forgetting and class-wise forgetting is that the --class_wise parameter is added to the command, which specifies the class number that we want to forget. Also, the default --forget_percentage is 0.5, which means that we forget 50% of the data from the specified class.