FELT Labs
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About
FELT Labs is a tool focused on enabling privacy-preserving data science through federated learning. It allows users to run machine learning and analytics on distributed datasets without direct access to the data itself, protecting user privacy. The platform facilitates the creation of decentralized federated learning projects and supports model training easily. Key features include encrypted exchange of model results, a nocode platform for running federated learning, and integrations with existing web applications through an SDK. Additionally, FELT Labs offers infrastructure setups for compute environments, consulting services for data science, and tools for organizations to manage their projects.
Platform
Task
Features
• decentralized federated learning project creation
• privacy-preserving data science
• encrypted model results exchange
• nocode platform for easier usage
• federated learning as a service integration
• consulting services for data science
• infrastructure for running compute jobs
FAQs
What is FELT Labs?
FELT Labs is a platform for privacy-preserving data science and federated learning.
How does the exchange of the model work?
We use compute jobs provided by Ocean protocol. The results of the compute job (models) are stored at data provider machine. And it is accessed via URLs that must be signed by the person who started the compute job.
Where to report issues?
In case you find any issues, you can create an issue in our project repository on GitHub.
Need personalised solution? Want to contact core developers?
We recommend contacting us via Discord or email for personalized solutions.
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