Update README.rst

README.rst

@@ -14,14 +14,14 @@ 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: 
 
+-  **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.
+
 -  **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.  
 
 -  **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.
 
 -  **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. 
 
--  **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.
-
 -  **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 of all necessary server-side components 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>`__.