diff --git a/README.rst b/README.rst index 7c32fc7bd..c0fbc2836 100644 --- a/README.rst +++ b/README.rst @@ -12,19 +12,21 @@ FEDn: An enterprise-ready federated learning framework ------------------------------------------------------- -Our goal is to provide a federated learning framework that is both secure, scalable and easy to use. We believe that that minimal code change should be needed to progress from early proof-of-concepts to production. This is reflected in our core design principles: +Our goal is to provide a federated learning framework that is both secure, scalable and easy-to-use. We believe that that minimal code change should be needed to progress from early proof-of-concepts to production. This is reflected in our core design: -- **Data-scientist friendly**. A ML-framework agnostic design lets data scientists implement use-cases using their framework of choice. A UI and a Python API enables users to manage complex FL experiments and track metrics in real time. +- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide a UI, REST API and a Python interface to help users manage FL experiments and track metrics in real time. -- **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (JWT) provides flexible integration in a range of production environments. +- **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) provides flexible integration in a range of production environments. -- **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. Reference deployment here: https://fedn.scaleoutsystems.com. +- **ML-framework agnostic**. A black-box client-side architecture lets data scientists interface with their framework of choice. + +- **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. - **Scalability and resilience.** Multiple aggregation servers (combiners) can share the workload. FEDn seamlessly recover from failures in all critical components and manages intermittent client-connections. -- **Developer friendly.** Extensive event logging and distributed tracing enables developers to monitor the sytem in real-time, simplifying troubleshooting and auditing. +- **Developer and DevOps friendly.** Extensive event logging and distributed tracing enables developers to monitor the sytem in real-time, simplifying troubleshooting and auditing. Extensions and integrations are facilitated by a flexible plug-in architecture. -We provide a fully managed deployment free of charge for for testing, academic, and personal use. Sign up for a `FEDn Studio account `__ and take the `Quickstart tutorial `__. +We provide a fully managed deployment for testing, academic, and personal use. Sign up for a `FEDn Studio account `__ and take the `Quickstart tutorial `__ to get started with FEDn. Features ========= @@ -62,11 +64,11 @@ Getting started Get started with FEDn in two steps: -1. Sign up for a `Free FEDn Studio account `__ +1. Register for a `FEDn Studio account `__ 2. Take the `Quickstart tutorial `__ -FEDn Studio (SaaS) is free for academic use and personal development / small-scale testing and exploration. For users and teams requiring -additional project resources, dedicated support or other hosting options, `explore our plans `__. +Use of our multi-tenant, managed deployment of FEDn Studio (SaaS) is free forever for academic research and personal development/testing purposes. +For users and teams requiring additional resources, more storage and cpu, dedicated support, and other hosting options (private cloud, on-premise), `explore our plans `__. Documentation =============