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https://scaleoutsystems.github.io/fedn/

FEDn: An open framework and a managed platform for real-world federated machine learning.
https://scaleoutsystems.github.io/fedn/

edge-ai edge-ml federated-learning federated-machine-learning fedml fleet-learning keras-tensorflow pytorch scikit-learn tensorflow

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FEDn: An open framework and a managed platform for real-world federated machine learning.

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README 

        
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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 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.  



-  **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. 



-  **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 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. 



Features

=========



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.)

- UI, CLI and Python API.

- Implement clients in any language (Python, C++, Kotlin etc.)

- No open ports needed client-side.



From development to FL in production: 



-  Secure deployment of server-side / control-plane on Kubernetes.

-  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 client APIs:



- Python client (this repository)

- C++ client (`FEDn C++ client `__)

- Android Kotlin client (`FEDn Kotlin client `__)



Getting started

============================



Get started with FEDn in two steps:  



1. Register for a `FEDn Studio account `__

2. Take the `Quickstart tutorial `__



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

=============



More details about the architecture, deployment, and how to develop your own application and framework extensions are found in the documentation:



-  `Documentation `__



FEDn Studio Deployment options

==============================



Several hosting options are available to suit different project settings.



-  `Public cloud (multi-tenant) `__: Managed multi-tenant deployment in public cloud. 

-   Dedicated cloud (single-tenant): Managed, dedicated deployment in a cloud region of your choice (AWS, GCP, Azure, managed Kubernetes) 

-   Self-managed: Set up a self-managed deployment in your VPC or on-premise Kubernets cluster using Helm Chart and container images provided by Scaleout. 



Contact the Scaleout team for information.



Support

=================



Community support is available in our `Discord

server `__.



Options are available for `Dedicated/custom support `__.



Making contributions

====================



All pull requests will be considered and are much appreciated. For

more details please refer to our `contribution

guidelines `__.



Citation

========



If you use FEDn in your research, please cite:



::



   @article{ekmefjord2021scalable,

     title={Scalable federated machine learning with FEDn},

     author={Ekmefjord, Morgan and Ait-Mlouk, Addi and Alawadi, Sadi and {\AA}kesson, Mattias and Stoyanova, Desislava and Spjuth, Ola and Toor, Salman and Hellander, Andreas},

     journal={arXiv preprint arXiv:2103.00148},

     year={2021}

   }



License

=======



FEDn is licensed under Apache-2.0 (see `LICENSE `__ file for

full information).



Use of FEDn Studio is subject to the `Terms of Use `__.