diff --git a/README.rst b/README.rst index 0bbac819a..47e98883d 100644 --- a/README.rst +++ b/README.rst @@ -15,25 +15,25 @@ FEDn enables developers, researchers and data scientists to build federated lear Core Features ============= -- **Scalable and resilient.** FEDn enables multiple aggregation servers (combiners) to divide up the work to coordinate clients and aggregate models. This makes the framework able to scale to large numbers of clients. - The server-side is able to seamlessly recover from failure, making for robust deployment in production scenarios. FEDn is robust in asynchronous federated learning scenarios, seamlessly handling clients that connects - and drops out during training. +- **Scalable and resilient.** FEDn enables multiple aggregation servers to share the work to coordinate clients and aggregate models. This makes the framework scalable to large numbers of clients. + The system is able to seamlessly recover from failure, enabling robust deployment in production. FEDn also handles asynchronous federated learning scenarios, where clients connect + and drop out during training. -- **Security**. FL clients do not have to open any ingress ports. The framework is built using secure industry standard communication protocols and +- **Security**. FL clients do not have to open any ingress ports, enabling real-world deployments in a wide range of settigs. Further, FEDn is implemented using secure industry standard communication protocols and supports token-based authentication for FL clients. - **Track events and training progress in real-time**. Extensive event logging and distributed tracing helps developers monitor experiments in real-time, facilitating troubleshooting and auditing. - Tracking and model validation data can easily be retrieved using the API enabling development of custom dashboards and visualizations. + Machine learning validation metrics from clients can be retrieved using the API, enabling flexible analysis of federated experiments. - **ML-framework agnostic**. FEDn is compatible with all major ML frameworks. Examples for Keras, PyTorch and scikit-learn are available out-of-the-box. -- **Deploy your FL project to production on FEDn Studio**. Users can develop their FL use-case in a local development environment and then deploy it to production on FEDn Studio. FEDn Studio - provides the FEDn server-side as a managed service. A web application provides an intuitive UI for orchestrating runs, visualizing and downloading results, and manage FL client tokens. +- **Deploy your FL project to production on FEDn Studio**. Users can develop a FL use-case in a local development environment, and then deploy it to production on FEDn Studio. FEDn Studio + provides the FEDn server-side as a managed service on Kubernetes. A web application provides an intuitive UI for orchestrating runs, visualizing and downloading results, and manage FL client tokens. -Getting started +Getting started with the SDK =============== The best way to get started with the FEDn SDK is to take the quickstart tutorial: @@ -47,17 +47,15 @@ You find more details about the architecture, deployment and how to develop your - `Documentation `__ -FEDn Studio +Deploying a project to FEDn Studio =============== -You can deploy your FEDn projects to FEDn Studio. Studio provides a managed, production-grade deployment of the FEDn server-side. With Studio you manage token-based authentication for clients, and are able to collaborate with other users in joint project workspaces. In addition to a REST API, Studio has an intuitive Dashboard that let's you manage FL exepriments and visualize and download logs and metrics. Follow this guide to `Deploy you project to FEDn Studio `__ . +Studio provides a managed, production-grade deployment of the FEDn server-side. With Studio you manage token-based authentication for clients, and are able to collaborate with other users in joint project workspaces. In addition to a REST API, Studio has an intuitive Dashboard that let's you manage FL experiments and visualize and download logs and metrics. Follow this guide to `Deploy you project to FEDn Studio `__ . Making contributions ==================== -All pull requests will be considered and are much appreciated. Reach out -to one of the maintainers if you are interested in making contributions, -and we will help you find a good first issue to get you started. For +All pull requests will be considered and are much appreciated. For more details please refer to our `contribution guidelines `__.