KaiyuanZh / FLIP

FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning [ICLR‘23, Best Paper Award at ECCV’22 AROW Workshop]

FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning

Table of Contents

• Overview
• Paper
• Workshop Slides
• Requirements
• Experiments
  • Install required packages
  • MNIST
  • Fashion-MNIST
  • CIFAR-10
• Code Architecture
• Citation
• Acknowledgement

Overview

• This is the PyTorch implementation for ICLR 2023 paper "FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning".
• This paper also wins a Best Paper Award 🏆 at ECCV 2022 AROW Workshop.
• [openreview] | [arXiv] | [workshop slides]

Requirements

• Python >= 3.7.10
• PyTorch >= 1.7.1
• TorchVision >= 0.8.2
• PyYAML >= 6.0

Experiments

• Pre-trained clean models prepared in the directory ./saved_models/.
• You can also train from round 0 to obtain the pre-trained clean models.
• Run the following commands to reproduce the results in the paper.

Install required packages

# Create python environment (optional)
conda env create -f environment.yml
source activate flip

Prior to federated training and inverting, make sure to create a data directory ./data/ and set the correct parameters in the .yaml file for single-shot or continuous FL backdoor attacks. Parameters can be found in the ./utils/xx.yaml directory.

MNIST

python main.py --params utils/mnist.yaml

Fashion-MNIST

python main.py --params utils/fashion_mnist.yaml

CIFAR-10

python main.py --params utils/cifar.yaml

Code Architecture

.
├── data                            # data directory
├── models                          # model structures for different datasets
├── saved_models                    # pre-trained clean models and saved models during training
│   ├── cifar_pretrain              # pre-trained clean models on CIFAR-10
│   ├── fashion_mnist_pretrain      # pre-trained clean models on Fashion-MNIST
│   └── mnist_pretrain              # pre-trained clean models on MNIST
├── utils                           # utils / params for different datasets
├── config.py                       # set GPU device and global variables
├── helper.py                       # helper functions
├── image_helper.py                 # helper functions for image datasets, e.g., load data, etc.
├── image_train.py                  # normal training and invert training
├── invert_CIFAR.py                 # benign local clients invert training on CIFAR-10
├── invert_FashionMNIST.py          # benign local clients invert training on Fashion-MNIST
├── invert_MNIST.py                 # benign local clients invert training on MNIST
├── main.py                         # main function, run this file to train and invert
├── test.py                         # test metrics
├── train.py                        # train function, image_train.py is called in this file
└── ...

Citation

Please cite our work as follows for any purpose of usage.

@inproceedings{
zhang2023flip,
title={{FLIP}: A Provable Defense Framework for Backdoor Mitigation in Federated Learning},
author={Kaiyuan Zhang and Guanhong Tao and Qiuling Xu and Siyuan Cheng and Shengwei An and Yingqi Liu and Shiwei Feng and Guangyu Shen and Pin-Yu Chen and Shiqing Ma and Xiangyu Zhang},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=Xo2E217_M4n}
}

Acknowledgement

• Backdoor Federated Learning
• DBA
• MOTH
• Neural Cleanse
• PGD