Stop Wrestling with Infrastructure.Start Your Federated Learning Project.
The code to train your model is ready, but training it across multiple organizations means navigating a complex maze of deployment, networking, and security. Stop wasting weeks on DevOps and start training your model.
We are building syft_flwr, the deployment engine for federated learning, and we are inviting a select group of teams to partner with us to accelerate their projects.
The Hard Part Isn’t the Model.
Setting up a real-world federated learning project often means…
The Challenge:
Manual Deployment
The Solution:
The SyftBox Way
Introducing syft_flwr: Your FL Deployment Engine.
syft_flwr is an open-source framework that handles the complex networking and orchestration layer of federated learning, letting you focus on what matters: your models and your data. It combines the flexibility of Flower to train federated models with the privacy and networking power of SyftBox.
Production-Ready Infrastructure, Out of the Box
Our flexible setup works with your existing data structures and compute environments. Eliminate the technical barriers to collaboration and get your project running on a robust, scalable foundation.
Find and Connect with New Partners
The built-in network allows you to securely discover and connect with other organizations who have complementary datasets for your research or industry project, turning a major challenge into a simple discovery process.
Already Using Flower?
Great! Keep your existing implementation. syft_flwr is fully compatible, allowing you to gain our powerful networking and access control features without rewriting your models.
Ready to Accelerate Your Project?
We are selecting a limited number of teams with active projects to become our first co-design partners. If you are ready to leave the infrastructure headaches behind, apply today.
Accepted partners will get dedicated support to implement syft_flwr for their active projects