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.

FEDn is free forever for academic and personal use / small projects. Sign up for a FEDn Studio account <https://fedn.scaleoutsystems.com/signup>__ and take the Quickstart tutorial <https://fedn.readthedocs.io/en/stable/quickstart.html>__ 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 <https://github.com/scaleoutsystems/fedn-cpp-client>__)
Android Kotlin client (FEDn Kotlin client <https://github.com/scaleoutsystems/fedn-android-client>__)

Getting started

Get started with FEDn in two steps:

Register for a FEDn Studio account <https://fedn.scaleoutsystems.com/signup>__
Take the Quickstart tutorial <https://fedn.readthedocs.io/en/stable/quickstart.html>__

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 <https://www.scaleoutsystems.com/start#pricing>__.

Documentation

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

Documentation <https://fedn.readthedocs.io>__

FEDn Project Examples

Our example projects demonstrate different use case scenarios of FEDn and its integration with popular machine learning frameworks like PyTorch and TensorFlow.

FEDn + PyTorch <https://github.com/scaleoutsystems/fedn/tree/master/examples/mnist-pytorch>__
FEDn + Tensforflow/Keras <https://github.com/scaleoutsystems/fedn/tree/master/examples/mnist-keras>__
FEDn + MONAI <https://github.com/scaleoutsystems/fedn/tree/master/examples/monai-2D-mednist>__
FEDn + Hugging Face <https://github.com/scaleoutsystems/fedn/tree/master/examples/huggingface>__
FEDn + Flower <https://github.com/scaleoutsystems/fedn/tree/master/examples/flower-client>__
FEDN + Self-supervised learning <https://github.com/scaleoutsystems/fedn/tree/master/examples/FedSimSiam>__

FEDn Studio Deployment options

Several hosting options are available to suit different project settings.

Public cloud (multi-tenant) <https://fedn.scaleoutsystems.com>__: 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 <https://discord.gg/KMg4VwszAd>__.

Options are available for Dedicated/custom support <https://www.scaleoutsystems.com/start#pricing>__.

Making contributions

All pull requests will be considered and are much appreciated. For more details please refer to our contribution guidelines <https://github.com/scaleoutsystems/fedn/blob/master/CONTRIBUTING.md>__.

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} }