diff --git a/README.rst b/README.rst index 2dc30d1e6..a29315a0c 100644 --- a/README.rst +++ b/README.rst @@ -12,17 +12,21 @@ FEDn -------- -FEDn empowers its users to create federated learning applications that seamlessly transition from proof-of-concepts to secure real-world distributed deployments. Leverage a fully managed service (SaaS) to quickly get started with zero deployment of service-side components. Seamlessly progress towards production by choosing from a range of deployment options, including private cloud and self-managed on your own infrastructure. +FEDn empowers its users to create federated learning applications that seamlessly transition from proof-of-concepts to secure real-world distributed deployments. Core design principles: -- **Seamless transition from proof-of-concepts to real-world FL**. No code change is required to go from development and testing to production. +- **Secure AND easy-to-use.** Federated learning aims to increase security and privacy in ML. FEDn is designed to Leverage a fully managed service (SaaS) to quickly get started with zero deployment of service-side components. -- **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. +- **Cloud native design**. Minimal code change should be required to go from development and testing to production. By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. -- **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. +- **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 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. +- **Secure by design.** FL clients do not need to open any ingress ports. Secure, industry-standard communication protocols (gRPC) and token-based authentication and RBAC (JWT) provides flexible integration in a range of production environments. + +- **ML-framework agnostic**. Use FEDn with your favorite ML framework. Examples available for + +- **Developer 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. Features @@ -42,14 +46,14 @@ Federated learning: FEDn Studio - 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 `__)