From R&D to real-world FL

The FEDn framework enables seamless development and deployment of federated learning applications, from local proofs-of-concept to distributed real-world settings. Start with your Federated Machine Learning (FML) project in a simulated local environment and move it to FEDn Studio for actual deployment, all without modifying your code.

Core features

Scalability and resilience
FEDn boosts federated learning by efficiently coordinating client models and aggregating them across several servers, catering to high client volumes and ensuring robust recovery from failures. It supports asynchronous training, smoothly handling client connectivity changes.
FEDn enhances security in federated learning environments by eliminating the need for clients to open ingress ports and using standard encryption protocols and token authentication. This approach streamlines deployment across varied settings and ensures secure, easy integration.
Real-time monitoring and analysis
With comprehensive event logging and distributed tracing, FEDn enables real-time monitoring of events and training progress, facilitating easier troubleshooting and auditing. The API offers access to machine learning validation metrics from clients, allowing for detailed analysis of federated experiments.
Framework agnostic
FEDn is designed to be ML-framework agnostic, seamlessly supporting major frameworks such as Keras, PyTorch, and scikit-learn. Ready-to-use examples are provided, facilitating immediate application across different ML frameworks.

Deployment models

FEDn is available in various deployment models to suit different project stages.
Software as a Service
Our service where you can manage projects and upload ML code and models without extensive DevOps involvement. This is ideal for early-stage projects, including pilots and proof-of-value phases.
For organizations with stringent cybersecurity needs, FEDn can be deployed on private clouds or on-premise, allowing full control over the deployment and enhanced security and privacy.

True FL from development to deployment

  • Develop FEDn projects locally, deploy via FEDn Studio.
  • Kubernetes-hosted FEDn for production.
  • Secure FL clients with token authentication and RBAC.
  • REST API for integration, dashboards for orchestration and results.
  • Admin tools for FEDn network management.
  • Collaborative data science in shared workspaces.
  • Flexible deployment: cloud or on-premise.

Get started

Begin with the SDK using our quick start guide, explore the documentation, and deploy in FEDn Studio.
The FEDn Software Development Kit
Start with the SDK
Start your journey by exploring the FEDn SDK with our comprehensive quickstart tutorial, an efficient and straightforward introduction.
The FEDn Studio
Deploy in FEDn Studio
Use FEDn Studio for managed deployments, featuring secure authentication, collaborative spaces, and a dashboard for experiment management and analysis.
Explore documentation
You find more details about the architecture, deployment and how to develop your own application in the documentation.
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