Deep FL Adversarial Attacks and Defenses

attacks and defenses on deep federated learning using and UAP and GANs.

Usage

Baseline Models

Viewable in baseline.py.

To create, train, and test a basic CNN baseline perform the following:

basic_baseline = BasicBaseline() # initialize a basic CNN
basic_baseline.load_data() # load the FashionMNIST data
basic_baseline.configure_attack(attack) # configure attack 
basic_baseline.train(num_epochs, lr) # train the model for num_epochs epochs and at learning rate lr
basic_baseline.test() # test the accuracy of the model

To create, train, and test a federated CNN baseline perform the following:

federated_baseline = FederatedBaseline(num_clients) # initialize a FL framework with num_clients clients training CNNs
federated_baseline.load_data() # load the FashionMNIST data
federated_baseline.configure_attack(attack, num_malicious) # configure num_malicious attackers using attack
federated_baseline.configure_defense(defense) # configure a defense model
federated_baseline.train(num_epochs, rounds, lr) # train the global model for num_epochs epochs, rounds rounds over the clients, and at learning rate lr
federated_baseline.test() # test the accuracy of the global model

Attacks

viewable in utils/attacks.py.

for no attack:

attack = NoAttack()

for random attack:

attack = RandomAttack(num_classes) # where num_classes is the total number of classes in the data

for targeted attack:

attack = TargetedAttack(target_label, class_label) # set training labels of target_label to class_label

for UAP attack:

attack = UAPAttack(target_label)

for GAN attack:

attack = GANAttack(client_model)

note that the GAN attack differs from the others as this does not affect the global model but instead aims to reconstruct user data. Thus its usage varies from the aforementioned attacks.

Defenses

viewable in utils/defenses.py

for no defenese:

defense = NoDefense()

for flipped labels defense:

defense = FlippedLabelsDefense(num_classes) # where num_classes denotes source class comparisons

Running baselines from bash

To run the basic baseline:

python3 baseline.py basic

To run the federated baseline:

python3 baseline.py federated

Future work

1. finish GAN attack implementation
2. create a main.py file to be able to run everything from the command line with flag arguments
3. reconfigure to work smoothly both on cpu and cuda

References

client data reconstruction: https://github.com/Jaskiee/GAN-Attack-against-Federated-Deep-Learning

UAP attack: https://github.com/phibenz/uap_virtual_data.pytorch

label flipping defense: https://arxiv.org/pdf/2007.08432v2.pdf (code at https://github.com/git-disl/DataPoisoning_FL)