ege-erdogan/splitout

SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection

Citation

@article{erdogan2023splitout,
  title={SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection},
  author={Erdogan, Ege and Teksen, Unat and Celiktenyildiz, Mehmet Salih and Kupcu, Alptekin and Cicek, A Ercument},
  journal={arXiv preprint arXiv:2302.08618},
  year={2024}
}

How to Run

Make sure that all files in the same directory:

• models.py
• sg_ad.ipynb
• util.py

Requirements

Make sure you have the following libraries installed:

numpy
torch
torchvision
scipy
tqdm
matplotlib
pandas

Running

1. Upload Collected Gradients

start executing the sg_ad.ipynb notebook. Before running the detection mechanism, ensure you upload your collected gradients to the following path from the cell under the header 1) Loading FSHA and honest gradients:

drive_path = '/content/drive/MyDrive/grads'

2. Start Running SplitOut

Continue with executing the notebook from the cell under the header 3) Converting Tensor gradients to NumPy Array.

3. Update SplitOut Parameters

Adjust the following parameters in the notebook to fit your needs from 4) Selecting desired reduced data rate and continue executing all cells until the end of the notebook.

• data_rate_honest_gradients
• window_size_list

Updates

24.06.2024: Our code is released.