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

arXiv: https://arxiv.org/abs/2503.04318 · HTML: https://mlsanity.com/paper/2503.04318/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [TensorFlow.js: Machine Learning for the Web and Beyond](https://mlsanity.com/paper/1901.05350/) (2019)
- [Moving Deep Learning into Web Browser: How Far Can We Go?](https://mlsanity.com/paper/1901.09388/) (2019)
- [Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG](https://mlsanity.com/paper/2412.16086/) (2024)
- [Collaborative Machine Learning Model Building with Families Using Co-ML](https://mlsanity.com/paper/2304.05444/) (2023)

## Abstract (arXiv)

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.