Differentially Private Decentralized Learning with Random Walks

Here is the code used to generate figures.

Requirements

numpy networkx matplotlib tqdm scipy

for the houses part: sklearn Pathlib typer

Datasets

The Facebook ego graph can be downloaded here: https://snap.stanford.edu/data/ego-Facebook.html and should be placed in a folder named 'facebook' in the project folder. The Housing dataset should be downloaded via the 'data' script, but can be manually retrieved there in case of problem: https://www.openml.org/d/823

Organization

• The file synthemuffcomparison.py enables to reproduce figure 1
• The folder Houses enable to reproduce the experiments of Figure 2
• The files south.py and fb.py enable to reproduce Figure 3

The code is adapted from the code of Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging..