From 30360de886700cbbc66404aba4b3779288ce5670 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Wed, 17 Jul 2024 11:06:40 +0200 Subject: [PATCH] Docs/SK-000 | Update main readme (#652) * Update README.rst Update main readme to clarify use of Studio a bit more. * 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 * Update README.rst * Update README.rst * Update README.rst * Update README.rst * Update README.rst * Update README.rst * Update README.rst --- README.rst | 30 ++++++++++++++---------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/README.rst b/README.rst index a13d7463f..7c32fc7bd 100644 --- a/README.rst +++ b/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 `__ and take the `Quickstart tutorial `__. 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 `__)