DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning
Abstract
Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the statistical power of predictive models, but creates an accessibility bias, where accuracy becomes inequitably distributed to those who have the resources to overcome these concerns. We present DISCO: an open-source DIStributed COllaborative learning platform accessible to non-technical users, offering a means to collaboratively build machine learning models without sharing any original data or requiring any programming knowledge. DISCO's web application trains models locally directly in the browser, making our tool cross-platform out-of-the-box, including smartphones. The modular design of \disco offers choices between federated and decentralized paradigms, various levels of privacy guarantees and several approaches to weight aggregation strategies that allow for model personalization and bias resilience in the collaborative training. Code repository is available at https://github.com/epfml/disco and a showcase web interface at https://discolab.ai
Cite
@article{arxiv.2511.19750,
title = {DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning},
author = {Julien T. T. Vignoud and Valérian Rousset and Hugo El Guedj and Ignacio Aleman and Walid Bennaceur and Batuhan Faik Derinbay and Eduard Ďurech and Damien Gengler and Lucas Giordano and Felix Grimberg and Franziska Lippoldt and Christina Kopidaki and Jiafan Liu and Lauris Lopata and Nathan Maire and Paul Mansat and Martin Milenkoski and Emmanuel Omont and Güneş Özgün and Mina Petrović and Francesco Posa and Morgan Ridel and Giorgio Savini and Marcel Torne and Lucas Trognon and Alyssa Unell and Olena Zavertiaieva and Sai Praneeth Karimireddy and Tahseen Rabbani and Mary-Anne Hartley and Martin Jaggi},
journal= {arXiv preprint arXiv:2511.19750},
year = {2025}
}