What is FELT Labs?

FELT Labs is a data science company that provides a suite of tools for working with private and distributed data. Our focus is on federated learning, which allows you to train machine learning models or perform data analytics across multiple datasets while preserving data privacy. We built our solution on top of Ocean Protocol, that creates tools for the web3 data economy. This means you can easily select and use datasets from Ocean. Our web application simplifies the entire training process, making it easy for users to get started quickly.

Federated Learning and How FELT Works

Federated learning is a powerful technique that enables multiple parties to collaborate on training a single machine learning model while keeping their private data secure. For example, three separate companies might want to create a machine learning model to improve their product recommendations, but they don't want to share their data with each other. With federated learning, each company trains a local model on its own data. These local models are then combined to create a global model that's better than any of the local models individually, while ensuring that no sensitive data is revealed during the process.

There are many applications where companies/individuals can make use of federated learning:

  • Car manufactures working on self-driving tehcnology

  • Hospitals developing AI to treat patients better

  • Individuals sharing data from fitness tracking devices

  • Ecommerce providing better products to customers

FELT makes federated learning simple by providing its own algorithms for training and aggregating models. We rely on Ocean protocol to handle everything around data management. Our platform allows data scientists to easily select any compatible data published on Ocean and use it to train their models. They can choose our algorithms or create their own for their specific use case, and run them seamlessly through FELT. Meanwhile, data providers can set prices on their data and get paid for providing compute to their private data.

There are multiple approaches to federated learning. At the moment, FELT implements only one of them, but we have plans to extend this in the future.


Key benefits of FELT:

  1. Secure - All data remains securely on the data provider machine. Access to data is protected by a blockchain network.

  2. Encrypted - All trained models are encrypted and exchanged only between interested parties. This ensures that the models are kept confidential and that privacy is maintained throughout the process.

  3. Easy - FELT makes the entire process of federated learning simple and easy to use. With our web application, data scientists can easily select compatible datasets from Ocean, choose their preferred algorithms, and train their models seamlessly.

  4. Rewards - Data providers can set prices on their data and get paid for providing their data. This incentivizes data sharing and allows data providers to benefit from the use of their data.

By leveraging FELT's secure, encrypted, and easy-to-use platform, data scientists and data providers can unlock the power of federated learning and gain new insights from their data while maintaining privacy and security.

Getting Started

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Guides: Jump right in

Follow our handy guides to get started on the basics as quickly as possible:

Complete Getting StartedData Provider

Fundamentals: Dive a little deeper

Learn the fundamentals of FELT to get a deeper understanding of our main features:

Federated LearningFELT Architecture

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