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IBA: Towards Irreversible Backdoor Attacks in Federated Learning (NeurIPS'23)

1. Setup

  1. Set up the environment by installing the dependencies from the environment.yml file:
make install
  1. The pretrained models are stored at ./checkpoint.
  2. Logs will be saved at ./results.
  3. MNIST/CIFAR-10 datasets will be automatically downloaded. Please download and process Tiny-ImageNet-200 from https://github.com/AI-secure/DBA. The raw data could be downloaded using wget http://cs231n.stanford.edu/tiny-imagenet-200.zip. The checkpoint for imagenet can be downloaded here.

2. Running the Experiments

To reproduce the results presented in the paper, run the following commands:

./exps/iba_cifar10_fedavg.sh # IBA under FedAvg (fixed-freq) ./exps/iba_cifar10_fedavg_multikrum.sh # IBA with CIFAR10 under MultiKrum ./exps/iba_mnist_fedavg_multikrum.sh # IBA with MNIST under MultiKrum ./exps/iba_cifar10_fedavg_fixed_pool.sh # # IBA with CIFAR10 (fixed-pool)

You can modify the following parameters to reproduce different scenarios:

  • --dataset: Specify the dataset.
  • --model: Select the model.
  • --defense_method: Choose the defense method.
  • --attack_method: Select the attack method.
  • --attack_freq: Set the attack frequency.
  • --model_replacement: Enable or disable model replacement.
  • --attacker_pool_size: Number of attackers
  • --num_nets: Total number of clients

Please refer to the paper for detailed results and additional configurations.

Acknowledgements

This code is based on the setup for backdoor attack and defense in Federated Learning (FL) from the repository Attack of the Tails: Yes, You Really Can Backdoor Federated Learning.

Please cite the paper, as below, when using this repository:

@inproceedings{ nguyen2023iba, title={{IBA}: Towards Irreversible Backdoor Attacks in Federated Learning}, author={Dung Thuy Nguyen and Tuan Minh Nguyen and Anh Tuan Tran and Khoa D Doan and KOK-SENG WONG}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, year={2023}, url={https://openreview.net/forum?id=cemEOP8YoC} } 

License

This project is licensed under the MIT License.
You are free to use, modify, and distribute this software under the terms of the license.

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IBA: Towards Irreversible Backdoor Attacks in Federated Learning (Poster at NeurIPS 2023)

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