FLAME (Unofficial)

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 to run the code in issue.

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
Fashio-MNIST	CNN	Badnet	No Defence	99.92	84.15	IID
Fashio-MNIST	CNN	Badnet	FLAME	0.23	83.7	IID
Fashio-MNIST	CNN	Badnet	No Defence	99.33	81.95	Non-IID
Fashio-MNIST	CNN	Badnet	FLAME	0.18	80.95	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 badnet,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 

Emample with with the Fashoin-MNIST dataset:

python main_fed.py --dataset fashion_mnist --model cnn --attack badnet --lr 0.1 --malicious 0.1 --poison_frac 1.0 --local_ep 1 --local_bs 256 --attack_begin 0 --defence avg --epochs 200 --attack_label 5 --attack_goal -1 --trigger 'square' --triggerX 22 --triggerY 22 --iid 1 --tau 0.8 --frac 0.2

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'.