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RFU-SS

Representation forgetting unlearning

Overview

This repository contains the implementation of RFU-SS for Machine Unlearning via Representation Forgetting with Parameter Self-Sharing (TIFS 2023)

Prerequisites

python = 3.10.10
torch==2.0.0
torchvision==0.15.1
matplotlib==3.7.1
numpy==1.23.5

Running the experiments

  1. To run the RFU and RFU-SS on MNIST
python /RFU-SS/VIBU_with_backdoor/On_MNIST/temp.py
  1. To run the RFU and RFU-SS on CIFAR10
python /RFU-SS/VIBU_with_backdoor/On_CIFAR10/cifar10_test.py
  1. To run our reproduced and improved HFU and VBU on MNIST
python /RFU-SS/VIBU_with_backdoor/On_MNIST/temp.py
  1. To run our reproduced and improved HFU and VBU on CIFAR
python /RFU-SS/VIBU_with_backdoor/On_CIFAR10/cifar10_test.py

Citation

@ARTICLE{10312776,
  author={Wang, Weiqi and Zhang, Chenhan and Tian, Zhiyi and Yu, Shui},
  journal={IEEE Transactions on Information Forensics and Security}, 
  title={Machine Unlearning via Representation Forgetting With Parameter Self-Sharing}, 
  year={2024},
  volume={19},
  number={},
  pages={1099-1111},
  keywords={Data models;Training;Degradation;Optimization;Computational modeling;Mutual information;Task analysis;Machine unlearning;representation forgetting;multi-objective optimization;machine learning},
  doi={10.1109/TIFS.2023.3331239}
}

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representation forgetting unlearning with parameter self-sharing

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