DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning

TL;DR

DISCO is a browser-based platform enabling privacy-preserving, collaborative machine learning without data sharing, accessible to non-technical users across devices, supporting various privacy and aggregation options.

Contribution

It introduces a user-friendly, open-source web tool for distributed collaborative learning that operates entirely in the browser, supporting multiple paradigms and privacy levels.

Findings

Supports federated and decentralized learning paradigms

Operates fully in-browser on various devices including smartphones

Provides customizable privacy and aggregation strategies

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