FLAME

Unofficial implementation for paper FLAME: Taming Backdoors in Federated Learning, if there is any problem, please let me know.

paper FLAME: Taming Backdoors in Federated Learning is from https://www.usenix.org/system/files/sec22-nguyen.pdf

Please contact me if you have any difficulty running the code in Issues.

Backdoor in FL

Our recent paper "Backdoor Federated Learning by Poisoning Backdoor-critical Layers" has been accepted in ICLR'24, please refer to the Github repo.

Results

Here ASR indicates attack success rate also called backdoor success rate, and Acc indicates accuracy of the main tasks.

Dataset	Model	Attack	Defence	ASR	Acc	iid
CIFAR-10	ResNet18	Badnet	No Defence	70.2	80.38	IID
CIFAR-10	ResNet18	Badnet	FLAME	3.33	78.77	IID
CIFAR-10	ResNet18	Badnet	No Defence	70.53	77.58	Non-IID
CIFAR-10	ResNet18	Badnet	FLAME	7.22	76.04	Non-IID

Requirement

Python=3.9

pytorch=1.10.1

scikit-learn=1.0.2

opencv-python=4.5.5.64

Scikit-Image=0.19.2

matplotlib=3.4.3

hdbscan=0.8.28

jupyterlab=3.3.2

Install instruction are recorded in install_requirements.sh

Run

VGG and ResNet18 can only be trained on CIFAR-10 dataset, while CNN can only be trained on fashion-MNIST dataset.

python main_fed.py      --dataset cifar,fashion_mnist \
                        --model VGG,resnet,cnn \
                        --attack baseline,dba \
                        --lr 0.1 \
                        --malicious 0.1 \
                        --poison_frac 1.0 \
                        --local_ep 2 \
                        --local_bs 64 \
                        --attack_begin 0 \
                        --defence avg,fltrust,flame,krum,RLR \
                        --epochs 200 \
                        --attack_label 5 \
                        --attack_goal -1 \
                        --trigger 'square','pattern','watermark','apple' \
                        --triggerX 27 \
                        --triggerY 27 \
                        --gpu 0 \
                        --save save/your_experiments \
                        --iid 0,1 

Images with triggers on attack process and test process are shown in './save' when running. Results files are saved in './save' by default, including a figure and a accuracy record. More default parameters on different defense strategies or attack can be seen in './utils/options'.