Code & supplementary material of the paper Label Inference Attacks Against Federated Learning on Usenix Security 2022.
Install Python 3.8 and Pytorch 1.7.0 +
Download the following datasets to './Code/datasets'.
CINIC-10 [1]
Yahoo answers dataset:
https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset
Criteo dataset:
https://labs.criteo.com/2014/02/download-kaggle-display-advertising-challenge-dataset/
Breast histopathology images:
https://www.kaggle.com/paultimothymooney/breasthistopathology-images
Tiny ImageNet:
https://www.kaggle.com/c/tiny-imagenet
Breast cancer wisconsin dataset:
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
CIFAR-10 or CIFAR-100:
Use pytorch built-in classes.
Use scripts in './Code/datasets_preprocess' to preprocess the datasets.
Use batch files in the './Code' folder.
'run_training.bat': train simulated VFL models.
'run_model_completion.bat': run the passive and active label inference attacks.
'run_direct_attack.bat': run the direct label inference attack.
'run_training_possible_defense.bat': test possible defenses against the passive and active label inference attacks.
'run_direct_attack_possible_defense.bat': test possible defenses against the direct label inference attack.
Use commands in the batch files, e.g., use commands in 'run_training.bat' to train simulated VFL models.
[1] L. N. Darlow, E. J. Crowley, A. Antoniou, and A. J. Storkey. CINIC-10 is not ImageNet or CIFAR-10. arXiv preprint arXiv:1810.03505, 2018.
It seems that many people do not understand the design of "keep_predict_loss" func in utils.py. Actually, this loss func is designed based on the chain rule, with the purpose of making sure that the gradient can be continually back-propagated to the weights of bottom models. Please see the derivation below.
