SyftJS

SyftJS is an open-source JavaScript library within the OpenMined ecosystem that provides privacy-preserving machine learning primitives for JavaScript environments. It is designed to run in web browsers and on Node.js, enabling data contributors to participate in model training and analysis without exposing raw data.

The library focuses on privacy-preserving techniques such as federated learning, secure multi-party computation, and differential privacy. Its architecture emphasizes data locality and secure orchestration of computations across distributed parties. Core concepts in SyftJS include workers, which represent data sources or collaborators, and plans or computations that describe the training or analysis logic. A messaging protocol coordinates computation among participants, allowing model updates to be aggregated while protecting individual data.

SyftJS aims to be interoperable with Python-based OpenMined tooling, such as PySyft, to support end-to-end privacy-preserving workflows across languages and platforms. This interoperability helps developers prototype and deploy privacy-preserving pipelines that span browser-based clients and back-end services.

The project is open-source and hosted on public repositories under a permissive license. It is part of broader efforts to bring privacy-preserving artificial intelligence to the web, enabling in-browser ML, secure collaboration among organizations, and experimentation with federated and privacy-preserving techniques. Documentation, tutorials, and examples accompany the project to assist developers in adopting its APIs and concepts. As an evolving project, users should consult the official repository for the latest features and API changes.