Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/scaleoutsystems/fedn

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

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

Last synced: about 1 month ago
JSON representation

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

Lists

README 

        
|pic1| |pic2| |pic3|



.. |pic1| image:: https://github.com/scaleoutsystems/fedn/actions/workflows/integration-tests.yaml/badge.svg

   :target: https://github.com/scaleoutsystems/fedn/actions/workflows/integration-tests.yaml



.. |pic2| image:: https://badgen.net/badge/icon/discord?icon=discord&label

   :target: https://discord.gg/KMg4VwszAd



.. |pic3| image:: https://readthedocs.org/projects/fedn/badge/?version=latest&style=flat

   :target: https://fedn.readthedocs.io



FEDn

--------



FEDn empowers its users to create federated learning applications that seamlessly transition from local proofs-of-concept to secure distributed deployments. 



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). 



We develop the FEDn framework following these core design principles:



-  **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. 



-  **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.



-  **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.



Features

=========



Core FL framework (this repository): 



- 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.

- 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: 



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

-  UI with dashboards for orchestrating experiments and 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. 



Getting started

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



Get started with FEDn in two steps:  



1. Sign up for a `Free 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 `__.  



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 `__.