# DataPoisoning_FL

**Repository Path**: codepool_admin/DataPoisoning_FL

## Basic Information

- **Project Name**: DataPoisoning_FL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-27
- **Last Updated**: 2024-08-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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}
}
```