From 5a27f1014153fd28533bf4e7e704fca5921dc832 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 11:26:46 +0200 Subject: [PATCH 01/11] Update readme --- README.rst | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 7c32fc7bd..d47c474b3 100644 --- a/README.rst +++ b/README.rst @@ -12,19 +12,21 @@ 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: +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. +- **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. -- **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. +- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a A UI and a Python API to help users manage FL experiments and track metrics in real time. -- **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. +- **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) 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. - **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 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 `__. +We provide a fully managed deployment for testing, academic, and personal use. Sign up for a `FEDn Studio account `__ and take the `Quickstart tutorial `__ to get started with FEDn. Features ========= From ff03d33fc655db19c49249c8242b134a86367824 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:17:22 +0200 Subject: [PATCH 02/11] Update README.rst --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index d47c474b3..ce1ccd92f 100644 --- a/README.rst +++ b/README.rst @@ -64,11 +64,11 @@ Getting started Get started with FEDn in two steps: -1. Sign up for a `Free FEDn Studio account `__ +1. Register for a `FEDn Studio account `__ 2. Take the `Quickstart tutorial `__ -FEDn Studio (SaaS) is free for academic use and personal development / small-scale testing and exploration. For users and teams requiring -additional project resources, dedicated support or other hosting options, `explore our plans `__. +Use of our multi-tenant, managed deployment of FEDn Studio (SaaS) is free forever for academic research and personal development/testing purposes. +For users and teams requiring additional resources, more storage and cpu, dedicated support, and other hosting options (private cloud, on-premise), `explore our plans `__. Documentation ============= From d76d08546e2f79b8b98d115019ab0907048e7238 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:18:20 +0200 Subject: [PATCH 03/11] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index ce1ccd92f..6680c2fb4 100644 --- a/README.rst +++ b/README.rst @@ -16,7 +16,7 @@ Our goal is to provide a federated learning framework that is both secure, scala - **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. -- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a A UI and a Python API to help users manage FL experiments and track metrics in real time. +- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a UI and a Python API to help users manage 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 (Jason Web Tokens) provides flexible integration in a range of production environments. From 82c9a1782f1af7a05315507489e1550f06611cc2 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:19:38 +0200 Subject: [PATCH 04/11] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 6680c2fb4..40cf05f98 100644 --- a/README.rst +++ b/README.rst @@ -16,7 +16,7 @@ Our goal is to provide a federated learning framework that is both secure, scala - **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. -- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a UI and a Python API to help users manage FL experiments and track metrics in real time. +- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a UI, a REST API and a Python interface to help users manage 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 (Jason Web Tokens) provides flexible integration in a range of production environments. From be5e43a394637f717b339cc1b545276ad83b2b7f Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:21:59 +0200 Subject: [PATCH 05/11] Update README.rst From 37ce978d580a66efd49d37b664589ab4a59922d0 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:22:20 +0200 Subject: [PATCH 06/11] Update README.rst --- README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.rst b/README.rst index 40cf05f98..9fdc2983f 100644 --- a/README.rst +++ b/README.rst @@ -14,10 +14,10 @@ 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: -- **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. - - **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a UI, a REST API and a Python interface to help users manage FL experiments and track metrics in real time. +- **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. + - **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) 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. From ea4746d74e53bbf3a3ff6f845a8faae6acb469ee Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:23:13 +0200 Subject: [PATCH 07/11] Update README.rst --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 9fdc2983f..9ffe3bfae 100644 --- a/README.rst +++ b/README.rst @@ -12,14 +12,14 @@ 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: +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: - **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a UI, a REST API and a Python interface to help users manage FL experiments and track metrics in real time. -- **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. - - **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) provides flexible integration in a range of production environments. +- **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. + - **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. - **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. From 0c4bddfbfba6256841b1005b2593341c7378507c Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:23:44 +0200 Subject: [PATCH 08/11] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 9ffe3bfae..1cd31d96b 100644 --- a/README.rst +++ b/README.rst @@ -18,7 +18,7 @@ Our goal is to provide a federated learning framework that is both secure, scala - **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) provides flexible integration in a range of production environments. -- **ML-framework agnostic**. A black-box client-side design lets data scientists implement use-cases using their framework of choice. +- **ML-framework agnostic**. A black-box client-side architecture lets data scientists implement use-cases using their framework of choice. - **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. From fd65e3647c14dc1401bb3112762f0e306d0feda8 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:24:06 +0200 Subject: [PATCH 09/11] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 1cd31d96b..9d3d02f05 100644 --- a/README.rst +++ b/README.rst @@ -18,7 +18,7 @@ Our goal is to provide a federated learning framework that is both secure, scala - **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) provides flexible integration in a range of production environments. -- **ML-framework agnostic**. A black-box client-side architecture lets data scientists implement use-cases using their framework of choice. +- **ML-framework agnostic**. A black-box client-side architecture lets data scientists using their framework of choice. - **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. From 9e7f1a025c90e51fb7ff4b4ae03050793045def6 Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:24:39 +0200 Subject: [PATCH 10/11] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 9d3d02f05..4e69bd863 100644 --- a/README.rst +++ b/README.rst @@ -18,7 +18,7 @@ Our goal is to provide a federated learning framework that is both secure, scala - **Secure by design.** FL clients do not need to open any ingress ports. Industry-standard communication protocols (gRPC) and token-based authentication and RBAC (Jason Web Tokens) provides flexible integration in a range of production environments. -- **ML-framework agnostic**. A black-box client-side architecture lets data scientists using their framework of choice. +- **ML-framework agnostic**. A black-box client-side architecture lets data scientists interface with their framework of choice. - **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. From 039b15900375218028db174fa5edf11da4b0490d Mon Sep 17 00:00:00 2001 From: Andreas Hellander Date: Tue, 23 Jul 2024 14:32:54 +0200 Subject: [PATCH 11/11] Update README.rst --- README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.rst b/README.rst index 4e69bd863..c0fbc2836 100644 --- a/README.rst +++ b/README.rst @@ -14,7 +14,7 @@ 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: -- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide both a UI, a REST API and a Python interface to help users manage FL experiments and track metrics in real time. +- **Minimal server-side complexity for the end-user**. Running a proper distributed FL deployment is hard. With FEDn Studio we seek to handle all server-side complexity and provide a UI, REST API and a Python interface to help users manage 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 (Jason Web Tokens) provides flexible integration in a range of production environments. @@ -24,7 +24,7 @@ Our goal is to provide a federated learning framework that is both secure, scala - **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 friendly.** Extensive event logging and distributed tracing enables developers to monitor the sytem in real-time, simplifying troubleshooting and auditing. +- **Developer and DevOps friendly.** Extensive event logging and distributed tracing enables developers to monitor the sytem in real-time, simplifying troubleshooting and auditing. Extensions and integrations are facilitated by a flexible plug-in architecture. We provide a fully managed deployment for testing, academic, and personal use. Sign up for a `FEDn Studio account `__ and take the `Quickstart tutorial `__ to get started with FEDn.