fluke: Federated Learning Utility frameworK for Experimentation and research

TL;DR

fluke is an open-source Python framework that simplifies the development and experimentation of federated learning algorithms, making it easier for researchers to prototype and extend FL systems.

Contribution

The paper introduces fluke, a flexible, easy-to-extend Python package tailored for prototyping federated learning algorithms, addressing limitations of existing frameworks.

Findings

Facilitates rapid development of FL algorithms

Supports easy extension with new algorithms

Reduces complexity and learning curve for researchers

Abstract

Since its inception in 2016, Federated Learning (FL) has been gaining tremendous popularity in the machine learning community. Several frameworks have been proposed to facilitate the development of FL algorithms, but researchers often resort to implementing their algorithms from scratch, including all baselines and experiments. This is because existing frameworks are not flexible enough to support their needs or the learning curve to extend them is too steep. In this paper, we present \fluke, a Python package designed to simplify the development of new FL algorithms. fluke is specifically designed for prototyping purposes and is meant for researchers or practitioners focusing on the learning components of a federated system. fluke is open-source, and it can be either used out of the box or extended with new algorithms with minimal overhead.