Publikation
InFL-UX: A Toolkit for Web-Based Interactive Federated Learning
Tim Maurer; Abdulrahman Mohamed Selim; Hasan Md Tusfiqur Alam; Matthias Eiletz; Michael Barz; Daniel Sonntag
In: Companion Proceedings of the 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems. ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS-2025), 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, located at EICS-2025, June 23-27, Trier, Germany, Pages 65-67, EICS '25 Companion (EICS '25 Companion), ISBN 9798400718663, Association for Computing Machinery, 6/2025.
Zusammenfassung
This paper presents InFL-UX, an interactive, proof-of-concept browser-based Federated Learning (FL) toolkit designed to integrate user contributions 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. InFL-UX bridges the gap between FL and interactive ML by prioritising usability and decentralised model training, empowering non-technical users to actively participate in ML classification tasks.