MEDfl 0.1.7

Python Open-source package for simulating federated learning and differential privacy

MEDfl : Federated Learning and Differential Privacy Simulation Tool for Tabular Data

Table of Contents

• 1. Introduction
• 2. Installation
• 3. Documentation
• 4. Getting started
• 5. Acknowledgement
• 6. Authors
• 7. Statement

1. Introduction

This Python package is an open-source tool designed for simulating federated learning and incorporating differential privacy. It empowers researchers and developers to effortlessly create, execute, and assess federated learning pipelines while seamlessly working with various tabular datasets.

2. Installation

Python installation

The MEDfl package requires python 3.9 or more to be run. If you don't have it installed on your machine, check out the following link Python . It also requires MySQL database

Package installation

For now, you can install the MEDflpackage as:

git clone https://github.com/MEDomics-UdeS/MEDfl.git
cd MEDfl
pip install -e .

MySQL DB configuration

MEDfl requires a MySQL DB connection, and this is in order to allow users to work with their own tabular datasets, we have created a bash script to install and configure A MySQL DB with phpmyadmin monitoring system, run the following command then change your credential on the MEDfl/scripts/base.py and MEDfl/scripts/config.ini files

sudo bash MEDfl/scripts/setup_mysql.sh

Project Base URL Update

Please ensure to modify the base_url parameter in the MEDfl/global_params.yaml file. The base_url represents the path to the MEDfl project on your local machine. Update this value accordingly.

3. Documentation

We used sphinx to create the documentation for this project. you can generate and host it locally by compiling the documentation source code using:

cd docs
make clean
make html

Then open it locally using:

cd _build/html
python -m http.server

4. Getting started

We have created a complete tutorial for the different functionalities of the package. it can be found here tutorial

5. Acknowledgment

MEDfl is an open-source package that welcomes any contribution and feedback. We wish that this package could serve the growing private AI research community.

6. Authors

• MEDomics: MEDomics consortium.

7. Statement

This package is part of https://github.com/medomics, a package providing research utility tools for developing precision medicine applications.

MIT License

Copyright (C) 2022 MEDomics consortium

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.