OpenMined

OpenMined is an open-source community and ecosystem focused on privacy-preserving artificial intelligence. It aims to advance techniques that allow machine learning on private or sensitive data without exposing that data, through approaches such as federated learning, secure multiparty computation, and differential privacy. The project emphasizes accessible tools, education, and collaboration to democratize privacy-preserving AI.

The initiative began in the mid- to late 2010s, founded by a group of researchers and developers including Andrew Trask and others who sought to create an open, collaborative environment for building and studying privacy-enhancing AI. It operates as a community-driven effort with a mission to lower barriers to adoption and to foster interoperability among privacy tools and educational resources.

Core software projects associated with OpenMined include PySyft, a Python library for privacy-preserving machine learning; PyGrid, a platform for deploying and coordinating private data collaborations; and CrypTen, a framework for secure multi-party computation. These tools are designed to work with popular machine learning frameworks and enable training and inference on encrypted or non-public data in distributed or remote settings.

OpenMined maintains its activity on open-source platforms, with documentation, tutorials, and community forums that invite participation from researchers, developers, and practitioners across academia and industry. The governance and direction of the project are largely community-driven, emphasizing collaboration over centralized control.

Overall, OpenMined seeks to accelerate innovation in privacy-preserving AI by providing accessible tooling, instructional material, and a collaborative ecosystem that connects developers, researchers, and organizations.