https://github.com/epfml/disco

DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.
https://github.com/epfml/disco

browser collaborative-learning decentralized-learning federated-learning machine-learning mobile privacy-preserving

Last synced: 4 days ago
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DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.

• Host: GitHub
• URL: https://github.com/epfml/disco
• Owner: epfml
• License: apache-2.0
• Created: 2020-06-23T23:20:33.000Z (over 4 years ago)
• Default Branch: develop
• Last Pushed: 2025-02-13T16:43:17.000Z (6 days ago)
• Last Synced: 2025-02-13T18:06:12.710Z (6 days ago)
• Topics: browser, collaborative-learning, decentralized-learning, federated-learning, machine-learning, mobile, privacy-preserving
• Language: TypeScript
• Homepage: https://discolab.ai
• Size: 14.4 MB
• Stars: 160
• Watchers: 12
• Forks: 28
• Open Issues: 65
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/CONTRIBUTING.md
  • License: LICENSE

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README

        


  

  


# **DISCO** - DIStributed COllaborative Machine Learning

DISCO leverages federated :star2: and decentralized :sparkles: learning to allow several data owners to collaboratively build machine learning models without sharing any original data.

The latest version is always running on the following link, for web and mobile:



   :man_dancing: https://discolab.ai/ :man_dancing:



___

:magic_wand: **DEVELOPERS:** DISCO is written fully in JavaScript/TypeScript. Have a look at our [developer guide](DEV.md).

___

:question: **WHY DISCO?** 

- To build deep learning models across private datasets without compromising data privacy, ownership, sovereignty, or model performance

- To create an easy-to-use platform that allows non-specialists to participate in collaborative learning

___

:gear: **HOW DISCO WORKS**

- DISCO has a *public model – private data* approach

- Private and secure model updates – *not data* – are communicated to either:

	- a central server : **federated** learning ( :star2: )

	- directly between users : **decentralized** learning ( :sparkles: ) i.e. no central coordination

- Model updates are then securely aggregated into a trained model

- See more [HERE](https://discolab.ai/#/information)

___

:question: **DISCO TECHNOLOGY** 

- DISCO runs arbitrary deep learning tasks and model architectures in your browser, via [TF.js](https://www.tensorflow.org/js)

- Decentralized learning :sparkles: relies on [peer2peer](https://github.com/feross/simple-peer) communication

- Have a look at how DISCO ensures privacy and confidentiality [HERE](docs/PRIVACY.md)

___

:test_tube: **RESEARCH-BASED DESIGN**

DISCO leverages latest research advances, enabling open-access and easy-use distributed training which is

- :lock: privacy-preserving ([R1](https://eprint.iacr.org/2017/281.pdf))

- :hammer_and_wrench: dynamic and asynchronous over time ([R2](https://arxiv.org/abs/2106.06639), [R7](https://arxiv.org/abs/2206.08307))

- :ninja: robust to malicious actors ([R3](https://arxiv.org/abs/2012.10333) (partially))

And more on the roadmap

- :tornado: efficient ([R4](https://github.com/epfml/powergossip), [R5](https://github.com/epfml/ChocoSGD))

- :lock: privacy-preserving while Byzantine robust ([R6](https://arxiv.org/abs/2006.04747))

- :ninja: resistant to data poisoning ([R8](https://arxiv.org/abs/2006.09365))

- :apple: :banana: interpretable in imperfectly interoperable data distributions ([R9](https://arxiv.org/abs/2107.06580))

- :mirror: personalizable ([R10](https://arxiv.org/abs/2103.00710))

- :carrot: fairly incentivizing participation

___

:checkered_flag: **HOW TO USE DISCO**

- Start by exploring our examples tasks in the [`DISCOllaboratives` page](https://discolab.ai/#/list). 

- The example DISCOllaboratives are based on popular ML tasks such as [GPT2](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf), [Titanic](https://www.kaggle.com/c/titanic), [MNIST](https://www.kaggle.com/c/digit-recognizer) or [CIFAR-10](https://www.kaggle.com/pankrzysiu/cifar10-python)

- It is also possible to create your own DISCOllaboratives without coding on the [custom training page](https://discolab.ai/#/create):

	- Upload the initial model

	- Choose between federated and decentralized for your DISCO training scheme ... connect your data and... done! :bar_chart:

	- For more details on ML tasks and custom training have a look at [this guide](./docs/TASK.md)