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: 7 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: 2025-01-21T17:23:45.000Z (18 days ago)
• Last Synced: 2025-01-23T03:08:48.513Z (17 days ago)
• Topics: browser, collaborative-learning, decentralized-learning, federated-learning, machine-learning, mobile, privacy-preserving
• Language: TypeScript
• Homepage: https://discolab.ai
• Size: 16.2 MB
• Stars: 159
• Watchers: 12
• Forks: 28
• Open Issues: 66
• 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, [directly in your browser](https://discolab.ai/), for web and mobile:

 :man_dancing: [https://discolab.ai/](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)