Docs/SK-000 | Update main readme (#665)

* Update readme

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

* Update README.rst

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 <https://fedn.scaleoutsystems.com/signup>`__  and take the `Quickstart tutorial <https://fedn.readthedocs.io/en/stable/quickstart.html>`__. 
+We provide a fully managed deployment for testing, academic, and personal use. Sign up for a `FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__  and take the `Quickstart tutorial <https://fedn.readthedocs.io/en/stable/quickstart.html>`__ 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 <https://fedn.scaleoutsystems.com/signup>`__
+1. Register for a `FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__
 2. Take the `Quickstart tutorial <https://fedn.readthedocs.io/en/stable/quickstart.html>`__
 
-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 <https://www.scaleoutsystems.com/start#pricing>`__.  
+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 <https://www.scaleoutsystems.com/start#pricing>`__.  
 
 Documentation
 =============