InFL-UX: A Toolkit for Web-Based Interactive Federated Learning

The lack of direct involvement of domain experts, due to technical barriers, further delays the acquisition of new training data [7]. To address these challenges, Fails and Olsen [6] introduced interactive machine learning (IML), enabling non-technical users to train ML models using their own data through manual classification or correcting model outputs. Unlike traditional ML, IML allows real-time updates in response to user input, facilitating focused and incremental adjustments [1, 5]. Building on these advancements, Tseng et al. [17] developed Co-ML, a tablet-based application for collaboratively building ML image classification models across multiple devices, focusing on teaching dataset design practices by creating a shared dataset. In this paper, we extend these concepts by proposing a browser-based tool that allows users to collaborate on IML tasks using federated learning.