This paper presents InFL-UX, an interactive, proof-of-concept browser-based Federated Learning (FL) toolkit designed to integrate user contributions seamlessly into the machine learning (ML) workflow. InFL-UX enables users across multiple devices to upload datasets, define classes, and collaboratively train classification models directly in the browser using modern web technologies. Unlike traditional FL toolkits, which often focus on backend simulations, InFL-UX provides a simple user interface for researchers to explore how users interact with and contribute to FL systems in real-world, interactive settings. By prioritising usability and decentralised model training, InFL-UX bridges the gap between FL and Interactive Machine Learning (IML), empowering non-technical users to actively participate in ML classification tasks.
View on arXiv@article{maurer2025_2503.04318,
title={ InFL-UX: A Toolkit for Web-Based Interactive Federated Learning },
author={ Tim Maurer and Abdulrahman Mohamed Selim and Hasan Md Tusfiqur Alam and Matthias Eiletz and Michael Barz and Daniel Sonntag },
journal={arXiv preprint arXiv:2503.04318},
year={ 2025 }
}