Docs/SK-000 | Update main readme

README.rst

@@ -9,50 +9,48 @@
 .. |pic3| image:: https://readthedocs.org/projects/fedn/badge/?version=latest&style=flat
    :target: https://fedn.readthedocs.io
 
-FEDn
---------
+FEDn: An enterprise-ready federated learning framework 
+-------------------------------------------------------
 
-FEDn empowers its users to create federated learning applications that seamlessly transition from local proofs-of-concept to secure distributed deployments. 
+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: 
 
-Leverage a flexible pseudo-local sandbox to rapidly transition your existing ML project to a federated setting. Test and scale in real-world scenarios using FEDn Studio - a fully managed, secure deployment of all server-side components (SaaS). 
+-  **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.
 
-We develop the FEDn framework following these core design principles:
+-  **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.  
 
--  **Seamless transition from proof-of-concepts to real-world FL**. FEDn has been designed to make the journey from R&D to real-world deployments as smooth as possibe. Develop your federated learning use case in a pseudo-local environment, then deploy it to FEDn Studio (cloud or on-premise) for real-world scenarios. No code change is required to go from development and testing to production. 
+-  **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.
 
--  **Designed for scalability and resilience.** FEDn enables model aggregation through multiple aggregation servers sharing the workload. A hierarchical architecture makes the framework well suited borh for cross-silo and cross-device use-cases. FEDn seamlessly recover from failures in all critical components, and manages intermittent client-connections, ensuring robust deployment in production environments.
+-  **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. 
 
--  **Secure by design.** FL clients do not need to open any ingress ports, facilitating distributed deployments across a wide variety of settings. Additionally, FEDn utilizes secure, industry-standard communication protocols and supports token-based authentication and RBAC for FL clients (JWT), providing flexible integration in production environments.   
-
--  **Developer and data scientist friendly.** Extensive event logging and distributed tracing enables developers to monitor experiments in real-time, simplifying troubleshooting and auditing. Machine learning metrics can be accessed via both a Python API and visualized in an intuitive UI that helps the data scientists analyze and communicate ML-model training progress.
+-  **Developer friendly.** Extensive event logging and distributed tracing enables developers to monitor the sytem in real-time, simplifying troubleshooting and auditing. 
 
+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>`__. 
 
 Features
 =========
 
-Core FL framework (this repository): 
+Federated learning: 
 
 - Tiered federated learning architecture enabling massive scalability and resilience. 
 - Support for any ML framework (examples for PyTorch, Tensforflow/Keras and Scikit-learn)
 - Extendable via a plug-in architecture (aggregators, load balancers, object storage backends, databases  etc.)
 - Built-in federated algorithms (FedAvg, FedAdam, FedYogi, FedAdaGrad, etc.)
-- CLI and Python API.
+- UI, CLI and Python API.
 - Implement clients in any language (Python, C++, Kotlin etc.)
 - No open ports needed client-side.
-- Flexible deployment of server-side components using Docker / docker compose.   
 
 
-FEDn Studio - From development to FL in production: 
+From development to FL in production: 
 
 -  Secure deployment of server-side / control-plane on Kubernetes.
--  UI with dashboards for orchestrating experiments and visualizing results
+-  UI with dashboards for orchestrating FL experiments and for visualizing results
 -  Team features - collaborate with other users in shared project workspaces. 
 -  Features for the trusted-third party: Manage access to the FL network, FL clients and training progress.
 -  REST API for handling experiments/jobs. 
 -  View and export logging and tracing information. 
 -  Public cloud, dedicated cloud and on-premise deployment options.
 
-Available clients:
+Available client APIs:
 
 - Python client (this repository)
 - C++ client (`FEDn C++ client <https://github.com/scaleoutsystems/fedn-cpp-client>`__)