The Sherpa.ai 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 Sherpa.ai 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: Sherpa.ai’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 Sherpa.ai 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: Sherpa.ai 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.