Privacy AI

Benefit from collaboration while ensuring data privacy, with Federated Learning

The Federated Learning and Differential Privacy Framework is a secure computing framework that provides a solution for collaboration, while protecting data privacy. Implement modular, agnostic Machine Learning that supports real-life business improvements. Federated Learning and Differential Privacy Framework

The Federated Learning and Differential Privacy Framework is a secure, open-source, Federated Learning computing framework that facilitates research and experimentation, implements secure computation protocols to enable regulation-compliant data collaboration, and provides a modular approach to enhance scalability using Federated frameworks, tools, and models. It learns models from decentralized data by training them locally, sharing the model-updated parameters, and securely aggregating the updated parameters to build a better model. The Federated Learning and Differential Privacy Framework aims to support 100% of the AI algorithms used in industry and can be applied in any sector, in order to ensure data privacy.

  • Business-Focused Models:’s technology supports real-life business improvements using clear methodologies, and provides the benefits of Machine Learning for problems that businesses face.
  • Agnostic Frameworks: Companies that focus on Deep Learning are very dependent on their models. The Federated Learning and Differential Privacy Framework is not specific to just one type of model or tool, but rather covers the entire spectrum of Machine Learning modeling techniques.
  • Democratizing Federated Learning: is focused on providing methodologies, pipelines, and evaluation techniques specifically designed for Federated Learning.
  • Privacy: Federated Learning is about ensuring privacy, which is done by providing a secure protocol, facilitating studies, and carrying out attack simulations. It supplies the benefits of Machine Learning, without having to worry about data being compromised.

Why the Federated Learning and Differential Privacy Framework?

Flexible and Adaptable

The Federated Learning and Differential Privacy Framework allows the automation of the end-to-end process for building, deploying, and maintaining Artificial Intelligence at scale in businesses systemsand devices. The modular approach is compatible with the entire spectrum of modeling techniques, which makes it easy to adapt to a variety of different business needs.

Unique Technology

The Federated Learning and Differential Privacy Framework is based on agnostic frameworks that allow for research and experimentation, using open-source computation. World-renowned market analysts,including Gartner and CB Insights, consider to be one of the leading companies in the virtual assistant and Artificial Intelligence sectors.


By working with data provided by companies and their users, the Federated Learning and Differential Privacy Framework ensures that organizations are able to understand what is most important to them. In order to give companies the ability to securely learn from sensitive, private information, allows full ownership of customer data, and protects it while facilitating collaborative learning.

Use Cases

Improve Diagnostics and Care Using Secure and Private Patient Data

Sensitive data from hospitals, doctors, insurance companies, and research institutes, concerning diseases, treatments, medications, genetics, and more is subject to data protection regulations. In order to learn from healthcare information securely, Federated Learning can be employed so that medical institutions can ensure data privacy, while providing the most advanced care possible.

Keep Funds Secure Without Sharing Customer Data

In the banking industry, Federated Learning can be used to identify money laundering transactions by using private transaction data to build more capable models. All banks using the same system benefit from each other’s transaction data, without exposing their own raw data to competitors.

Advance Research Using a Private Framework

Universities and research institutions can use Federated Learning to combine their efforts, while ensuring their data remains private, thanks to a Federated Framework. This structure for anonymous collaboration allows researchers the opportunity to benefit from their colleagues' data, advance their research, and amplify their findings.