FCBA

This repo is implementation for the accepted paper "Beyond Traditional Threats: A Persistent Backdoor Attack in Federated Learning" ( AAAI 2024 ).

Figure 1: Overview of full combination backdoor attack(FCBA) in FL. At round t + 1, the aggregator merges local data (both benign and adversarial) from t to update G_t+1. During a backdoor attack, the attacker uses trigger partition m to create local trigger patterns and identifies M maliciousclients, each with a unique trigger pattern.

Requirements

Python >= 3.7
Pytorch >= 1.12.1
numpy >= 1.21.5

We conducted experiments using two parallel NVIDIA GeForce RTX 3090 graphics cards.

Datasets

CIFAR10 will be automatically download

Folder Structure

.
├── data                        # Data Storage
├── models                      # Supported models
├── saved_models                # Save experimental data
├── utils                       # Experimental configuration folder
├── helper.py                   # a utility module designed to assist in the training and communication of deep learning models.
├── image_helper.py             # A helper class for processing image data, mainly used for loading, processing, and preparing data in deep learning models.
├── image_train.py              # Defined a function for training deep learning models
├── main.py                     # Federated framework main function
├── test.py                     # Test class functions to test model performance
├── train.py                    # Execute model training based on input parameters and configuration information

How to run:

• prepare the pretrained model: Our clean models for CIFAR10 have been placed in the directory \saved_models\cifar_pretrain. You can also train from the round 0 to obtain the pretrained clean model.
• we can use Visdom to monitor the training progress.

python -m visdom.server -p 8096

• set parameters properly, then run experiments for the datasets:

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

Optional Parameters

There are a several of optional arguments in the cifar_params.yaml:

• lr/poison_lr: Client/Malicious client learning rate.
• internal_poison_epochs : Benign client training rounds per round.
• internal_poison_clean_epochs : Malicious clients have training rounds per round.
• number_of_total_participants : Number of participating clients.
• scale_weights_poison : Malicious client amplifies parameters.
• total_list : List of Malicious Client Numbers.
• poisoning_per_batch : Number of poisoned images per batch.
• X_poison_pattern : Add position of the Xth trigger.
• no_models : Number of selected clients per round.
• X_poison_epochs : The X-th type trigger malicious client adds a round.
• dirichlet_alpha : If you choose to partition the client using the Dirichlet distribution, this is the coefficient.
• poison_label_swap : Malicious client target label.