dddkyi/DataPoisoning_FL

Code for Data Poisoning Attacks Against Federated Learning Systems

Data Poisoning Attacks Against Federated Learning Systems

Code for the ESORICS 2020 paper: Data Poisoning Attacks Against Federated Learning Systems

Installation

1. Create a virtualenv (Python 3.7)
2. Install dependencies inside of virtualenv (pip install -r requirements.pip)
3. If you are planning on using the defense, you will need to install matplotlib. This is not required for running experiments, and is not included in the requirements file

Instructions for execution

Using this repository, you can replicate all results presented at ESORICS. We outline the steps required to execute different experiments below.

Setup

Before you can run any experiments, you must complete some setup:

1. python3 generate_data_distribution.py This downloads the datasets, as well as generates a static distribution of the training and test data to provide consistency in experiments.
2. python3 generate_default_models.py This generates an instance of all of the models used in the paper, and saves them to disk.

General Information

Some pointers & general information:

• Most hyperparameters can be set in the federated_learning/arguments.py file
• Most specific experiment settings are located in the respective experiment files (see the following sections)

Experiments - Label Flipping Attack Feasibility

Running an attack: python3 label_flipping_attack.py

Experiments - Attack Timing in Label Flipping Attacks

Running an attack: python3 attack_timing.py

Experiments - Malicious Participant Availability

Running an attack: python3 malicious_participant_availability.py

Experiments - Defending Against Label Flipping Attacks

Running the defense: python3 defense.py

Experiment Hyperparameters

Recommended default hyperparameters for CIFAR10 (using the provided CNN):

• Batch size: 10
• LR: 0.01
• Number of epochs: 200
• Momentum: 0.5
• Scheduler step size: 50
• Scheduler gamma: 0.5
• Min_lr: 1e-10

Recommended default hyperparameters for Fashion-MNIST (using the provided CNN):

• Batch size: 4
• LR: 0.001
• Number of epochs: 200
• Momentum: 0.9
• Scheduler step size: 10
• Scheduler gamma: 0.1
• Min_lr: 1e-10

Citing

If you use this code, please cite the paper:

@ARTICLE{2020arXiv200708432T,
       author = {{Tolpegin}, Vale and {Truex}, Stacey and {Emre Gursoy}, Mehmet and
         {Liu}, Ling},
        title = "{Data Poisoning Attacks Against Federated Learning Systems}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Computer Science - Cryptography and Security, Statistics - Machine Learning},
         year = 2020,
        month = jul,
          eid = {arXiv:2007.08432},
        pages = {arXiv:2007.08432},
archivePrefix = {arXiv},
       eprint = {2007.08432},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200708432T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}