From c7a7aaea399ceb389a0e2e39ee515388cdac941d Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Wed, 10 Jul 2024 10:51:42 +0200 Subject: [PATCH] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 6ef036af4..2dc30d1e6 100644 --- a/README.rst +++ b/README.rst @@ -20,7 +20,7 @@ Core design principles: - **Designed for 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. +- **Secure by design.** FL clients do not need to open any ingress ports. FEDn utilizes secure, industry-standard communication protocols and supports token-based authentication and RBAC for FL clients usign Java Web Tokens (JWT), providing flexible integration in a range of 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.