FedModelKit 0.7.0

Tools to scaffold and run privacy-preserving federated learning experiments across distributed data sites.

🌐 FedModelKit (fmk)

FedModelKit is a Python library designed to streamline the execution of federated learning tasks within a network of collaborating partners.
It provides a guided workflow that helps users quickly set up experiments, manage dependencies, and follow step-by-step instructions through generated resources.

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📑 Table of Contents

• Overview
• Features
• Dependencies
• Installation
• Quick Start
• Next Steps

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🔎 Overview

Federated learning enables multiple partners to train machine learning models collaboratively without sharing raw data.
The FedModelKit library simplifies this process by:

• Automating project initialization
• Creating a reproducible experiment directory with all required dependencies
• Offering a clear, step-by-step workflow to run experiments in a federated network

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🧩 Dependencies

These are the dependencies of the packages, including the required Python version:

Dependency	Version	Description
Python	>=3.13	Required Python interpreter version
flwr[simulations]	==1.17.0	Federated learning framework and simulations with Flower
flwr_datasets	>=0.5.0	Datasets compatible with Flower
mlflow	>=3.1.1	For experiment tracking and logging
pandas	>=2.3.0	Data manipulation and analysis

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⚙️ Installation

It is recommended to install FedModelKit inside a virtual environment.
We suggest using uv for fast and reproducible setups:

1. Create and activate a virtual environment

   uv venv
   source .venv/bin/activate
   

   or in case of Windows OS

   uv venv
   .venv\Scripts\activate
   

2. Install FedModelKit

   uv pip install FedModelKit
   

🚀 Quick Start

Initialize a new federated learning experiment with:

fmk init -n my_experiment

This will generate in the present directory the project for a new FL experiment.

📖 Next Steps

👉 Once the experiment directory has been created, open the file README.md that has been generated in your directory and follow the guided workflow to set up and run your federated learning tasks.