syft-flwr:
Stop Wrestling with Infrastructure.
Start Your Federated Learning Project.
Production-ready federated learning infrastructure in days, not months. Get the governance, security, and collaboration tools stakeholders demand without custom engineering headaches.
The Hard Part Isn’t the Model, It’s the Production Infrastructure.
Setting up a real-world federated learning project often means weeks of infrastructure work before any ML happens.
- The Challenge:Manual Deployment
- Weeks coordinating VPNs & firewalls just to connect data sites. No way to find new partners.
- No separation of user roles, forcing you to build custom and complex authentication systems.
- No native way for data owners to approve code, requiring manual processes to track governance and satisfy compliance.
- No official, production-ready deployment method, leaving you to build everything from the ground up.
- The Solution:syft-flwr
- Auto-discovery of data sites and assets. A secure, zero-config network that just works.
- Built-in identity and access control with distinct roles for Data Owners and Data Scientists. Built in request, review and approval system.
- Built-in code approval workflows that give data owners control and create an immutable audit log for compliance.
- A simple, robust, and fully open-source deployment engine, ready for production with official Docker support.
Choose Your Deployment Path
syft-flwr works with your choice of transport layer. Same FL workflow, same governance features – pick what fits your environment.
- SyftBox
- Best for: Production deployments with IT support
- How it works: Server-coordinated network - code executes at data sites, only results sync
- Requirements: Use OpenMined relay server or self-host; Docker/K8s ready
- Zero-Setup P2P (Alpha)
- Best for: Zero-setup pilots, GDPR compliance, clinician-friendly environments
- How it works: Serverless architecture using familiar cloud storage (e.g. Google Drive) - no public server, no infrastructure to maintain
- Requirements: Just Google Colab + a Google account
Both options use the same syft-flwr framework. Your FL code works identically – only the transport layer differs.
Real-World Applications
Federated learning enables secure collaboration across organizations that can’t share raw data. Train models on distributed datasets while maintaining privacy, compliance, and trust between all parties involved.
Healthcare & Life Sciences:
Enable multi-hospital research consortia to train models on sensitive patient data for applications like cancer research while maintaining privacy.
Government & Research:
Allow academic consortia and government agencies to perform federated analytics across heterogeneous datasets without data pooling.
Financial Services:
Facilitate cross-enterprise fraud detection or federated credit scoring without sharing proprietary customer data.
Any Current Flower User
Teams with existing Flower code can leverage syft-flwr to deploy to a production environment in days, gaining governance, audit trails, and security features with minimal changes.
Any Federated Computations Use Cases on Distributed Data
More generally, syft-flwr can be used in use cases where a piece of code (computation) needs to be run on distributed machines (with their private data), then aggregate the outputs
Why Choose syft-flwr
Built-in Governance and Trust:
syft-flwr is designed for real-world trust limitations. Its code approval system allows data owners to see exactly what code will run on their data, fostering trust between collaborating organizations.Multi-Role User Management:
Simplified Data & Network Discovery:
Fully Open-Source for Maximum Transparency:
syft-flwr is completely open-source, ensuring that all code can be audited. This transparency is essential for gaining the trust of partners in a production environment, especially when dealing with sensitive data.Scientific Flexibility, Production Ready:
syft-flwr provides the production guardrails without limiting your research questions, whether you're running simple statistical tests or fine-tuning complex models.Core Capabilities:
Full Flower Integration:
Run unmodified Flower code with only 2-3 lines needed to connect to the SyftBox network.
Network Discovery:
Automatically detect participating data sites, eliminating manual node configuration.
Immutable Audit Logs:
A built-in record of all training rounds and approvals for compliance purposes.
Secure Data Storage & Management
Provides secure and efficient mechanisms for storing and managing sensitive data.
Deployment:
Docker & Kubernetes Ready:
Provides official Docker images and Helm charts for standardized, production-ready deployment, a foundational requirement for users.
Deploy Anywhere:
Works on-premise or in any major cloud environment (AWS, Azure, GCP).
Build with syft-flwr, your way
syft-flwr is ready for hands-on teams and curious tinkerers. Choose the path that fits your work.
For serious projects
Launch or scale a real federation project with guided support from our team.
For tinkerers and explorers
Kick the tires with docs and a walkthrough tutorial. Get unstuck fast in our community.
Get an invite to the OpenMined Slack community
Resources
Getting Started:
- Zero-Setup P2P Tutorial – Zero-Setup Federated Learning in Google Colab ⬩ OpenMined
- SyftBox Tutorial Series – Federated Learning for Diabetes Prediction: A Real-World Tutorial
NAIRR Partners:
- US-based researchers: Leverage NAIRR deep partnership resources: NAIRR Program ⬩ OpenMined