Disco

Disco

Decentralized Collaborative Machine Learning

A collection of objects and routines to help you develop your own distributed machine learning algorithm. It has two modes, federated, where a server is keeping track of the model, and decentralized, where each participant has their own local model. You can train your model directly in the browser, allowing it to run on a variety of hardware. Many extensions are in the pipeline, such as adding Byzantine resistance or training without revealing the model. Compared to other solutions, Disco is bridging the gap between people creating machine learning models and people having relevant data. By running without installation nor complex configuration, anyone can help. It's also a breeding ground for new machine learning technologies.

DecentralizedDistributed LearningTensorFlow
Key facts
Maturity
PrototypeIntermediateMature
Support
C4DT
Active
Lab
Active
  • C4DT work
  • Technical
  • Research papers
  • Presentation
  • Demo
Status: Active
Timeline: 2022/Q1 developing the project

Machine Learning and Optimization Laboratory

Machine Learning and Optimization Laboratory
Martin Jaggi

Prof. Martin Jaggi

The Machine Learning and Optimization Laboratory is interested in machine learning, optimization algorithms and text understanding, as well as several application domains.

This page was last edited on 2024-04-09.