The FEDn framework
The FEDn framework is designed to support the entire innovation journey from personal testing to proof-of-concepts to geographically distributed FL in the real-world. As a data scientist, bring your ML code and use FEDn to go from centralized to federated with no code change and minimal DevOps complexity.
FEDn 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.
Security
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.
Cloud native
By following cloud-native design principles, we ensure a flexible range of deployment options that cater to various needs, including private cloud environments, on-premise infrastructure, and hybrid setups. This approach ensures FEDn can be integrated across diverse deployment scenarios.
FEDn Studio overview
FEDn is available in various deployment models to suit different project stages.
Benefits
To develop FEDn projects locally and deploy them via FEDn Studio, you can leverage Kubernetes-hosted FEDn for production environments, ensuring secure federated learning (FL) clients through token authentication and role-based access control (RBAC).
Integration is facilitated via a REST API, while dashboards offer orchestration and results visualization. Admin tools are available for managing the FEDn network, enabling collaborative data science within shared workspaces. The deployment is flexible, supporting both cloud and on-premise setups.
To develop FEDn projects locally and deploy them via FEDn Studio, you can leverage Kubernetes-hosted FEDn for production environments, ensuring secure federated learning (FL) clients through token authentication and role-based access control (RBAC).
Integration is facilitated via a REST API, while dashboards offer orchestration and results visualization. Admin tools are available for managing the FEDn network, enabling collaborative data science within shared workspaces. The deployment is flexible, supporting both cloud and on-premise setups.
Flexible deployment options
FEDn Studio deploys to any standard Kubernetes cluster. This ensures flexible hosting options ranging from public and private cloud, to on-premise clusters and single workstations.
Software as a Service
We provide a fully managed cloud-hosted instance of FEDn. Requiering no server-side DevOps, this is the easiest way to get started with FL. By keeping all focus on the ML problem client-side, this is deal for early-stage projects, including pilots and proof-of-value phases.
Self-hosted
For organizations with stringent cybersecurity needs, FEDn can be deployed on private clouds / VPCs or on-premise, allowing full control over the deployment and enhanced security and privacy.
Framework agnostic
FEDn is designed to be ML-framework agnostic, seamlessly supporting major frameworks such as Keras, PyTorch, Tensorflow, Huggingface and scikit-learn. Ready-to-use examples are provided, facilitating immediate application across different ML frameworks.
FEDn framework interface
FAQ
Why should we choose your FL framework over other options?
Our framework offers an easy-to-use interface, visual aids, and collaboration tools for ML/FL projects, with features like distributed tracing and event logging for debugging and performance analysis. It ensures security through client identity management and authentication, and has scalable architecture with multiple servers and load-balancers. FEDn also allows flexible experimentation, session management, and deployment on any cloud or on-premises infrastructure.
Can we build our own IP using your framework?
Absolutely. You can develop your own IP without any conflict. Utilize our framework and Scaleout’s expertise to accelerate your project. There's no risk of lock-in, as our Software Development Kit (SDK) for integration is licensed under Apache2. We're confident you'll find value in our support services, warranty, indemnification, and comprehensive toolkit.
Is this yet another ML platform we have to install?
FEDn is a versatile framework that can be extended, configured, and integrated into existing systems to tailored to your environment. For effective Federated Learning (FL) management, deployment of server-side components and charts is necessary. It enhances rather than replaces your current setup.
How can I explore FL without deep technical expertise?
We offer a cloud-hosted FL platform for easy FL exploration, optimized for cost and ideal for R&D. Scaleout enables data scientists to investigate FL without initial IT/DevOps resources. We provide a smooth transition to self-hosted production with enterprise integrations, ensuring your PoC is scalable, secure, and representative of real-world scenarios.
What deployment options does FEDn offer?
FEDn offers two main deployment options to cater to different organizational needs and project stages. The fully-managed SaaS (Software as a Service) model simplifies access to federated learning technology, making it ideal for early-stage projects, pilots, and proof-of-value phases. For organizations with strict cybersecurity requirements, FEDn can be deployed on private clouds or on-premise, providing full control over deployment, security, and privacy. This self-managed option is particularly suitable for advanced security needs, such as protecting IP-sensitive ML models.
How can FEDn support my federated learning needs?
FEDn supports a range of capabilities to meet diverse organizational demands. It offers both multi-tenant and single-tenant options, allowing organizations to choose the configuration that best suits their needs. Additionally, FEDn takes care of complex operations management, including server aggregation, data storage, user authentication, network configuration, and system monitoring. This reduces the operational burden on users, enabling them to focus on developing and refining their federated learning.