| Name | fedn JSON |
| Version |
0.20.0
JSON |
| download |
| home_page | None |
| Summary | Scaleout Federated Learning |
| upload_time | 2024-12-04 11:37:47 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | Copyright 2021 Scaleout Systems AB. All rights reserved. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2020 Scaleout Systems AB Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
scaleout
fedn
federated learning
fl
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
|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: 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:
1. Register for a `FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__
2. 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}
}
License
=======
FEDn is licensed under Apache-2.0 (see `LICENSE <LICENSE>`__ file for
full information).
Use of FEDn Studio is subject to the `Terms of Use <https://www.scaleoutsystems.com/terms>`__.
Raw data
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"download_url": "https://files.pythonhosted.org/packages/c1/58/abc52883e6cbd34fba8781b4742ac33edbf35c1b69b2bec69a19c34b5127/fedn-0.20.0.tar.gz",
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"description": "|pic1| |pic2| |pic3|\n\n.. |pic1| image:: https://github.com/scaleoutsystems/fedn/actions/workflows/integration-tests.yaml/badge.svg\n :target: https://github.com/scaleoutsystems/fedn/actions/workflows/integration-tests.yaml\n\n.. |pic2| image:: https://badgen.net/badge/icon/discord?icon=discord&label\n :target: https://discord.gg/KMg4VwszAd\n\n.. |pic3| image:: https://readthedocs.org/projects/fedn/badge/?version=latest&style=flat\n :target: https://fedn.readthedocs.io\n\nFEDn: An enterprise-ready federated learning framework \n-------------------------------------------------------\n\nOur 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: \n\n- **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.\n\n- **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. \n\n- **ML-framework agnostic**. A black-box client-side architecture lets data scientists interface with their framework of choice. \n\n- **Cloud native.** By following cloud native design principles, we ensure a wide range of deployment options including private cloud and on-premise infrastructure. \n\n- **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. \n\n- **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. \n\nFEDn 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. \n\nFeatures\n=========\n\nFederated learning: \n\n- Tiered federated learning architecture enabling massive scalability and resilience. \n- Support for any ML framework (examples for PyTorch, Tensforflow/Keras and Scikit-learn)\n- Extendable via a plug-in architecture (aggregators, load balancers, object storage backends, databases etc.)\n- Built-in federated algorithms (FedAvg, FedAdam, FedYogi, FedAdaGrad, etc.)\n- UI, CLI and Python API.\n- Implement clients in any language (Python, C++, Kotlin etc.)\n- No open ports needed client-side.\n\n\nFrom development to FL in production: \n\n- Secure deployment of server-side / control-plane on Kubernetes.\n- UI with dashboards for orchestrating FL experiments and for visualizing results\n- Team features - collaborate with other users in shared project workspaces. \n- Features for the trusted-third party: Manage access to the FL network, FL clients and training progress.\n- REST API for handling experiments/jobs. \n- View and export logging and tracing information. \n- Public cloud, dedicated cloud and on-premise deployment options.\n\nAvailable client APIs:\n\n- Python client (this repository)\n- C++ client (`FEDn C++ client <https://github.com/scaleoutsystems/fedn-cpp-client>`__)\n- Android Kotlin client (`FEDn Kotlin client <https://github.com/scaleoutsystems/fedn-android-client>`__)\n\n\nGetting started\n============================\n\nGet started with FEDn in two steps: \n\n1. Register for a `FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__\n2. Take the `Quickstart tutorial <https://fedn.readthedocs.io/en/stable/quickstart.html>`__\n\nUse of our multi-tenant, managed deployment of FEDn Studio (SaaS) is free forever for academic research and personal development/testing purposes.\nFor 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>`__. \n\nDocumentation\n=============\n\nMore details about the architecture, deployment, and how to develop your own application and framework extensions are found in the documentation:\n\n- `Documentation <https://fedn.readthedocs.io>`__\n\nFEDn Project Examples\n=====================\n\nOur example projects demonstrate different use case scenarios of FEDn \nand its integration with popular machine learning frameworks like PyTorch and TensorFlow.\n\n- `FEDn + PyTorch <https://github.com/scaleoutsystems/fedn/tree/master/examples/mnist-pytorch>`__\n- `FEDn + Tensforflow/Keras <https://github.com/scaleoutsystems/fedn/tree/master/examples/mnist-keras>`__\n- `FEDn + MONAI <https://github.com/scaleoutsystems/fedn/tree/master/examples/monai-2D-mednist>`__\n- `FEDn + Hugging Face <https://github.com/scaleoutsystems/fedn/tree/master/examples/huggingface>`__\n- `FEDn + Flower <https://github.com/scaleoutsystems/fedn/tree/master/examples/flower-client>`__\n- `FEDN + Self-supervised learning <https://github.com/scaleoutsystems/fedn/tree/master/examples/FedSimSiam>`__\n\nFEDn Studio Deployment options\n==============================\n\nSeveral hosting options are available to suit different project settings.\n\n- `Public cloud (multi-tenant) <https://fedn.scaleoutsystems.com>`__: Managed multi-tenant deployment in public cloud. \n- Dedicated cloud (single-tenant): Managed, dedicated deployment in a cloud region of your choice (AWS, GCP, Azure, managed Kubernetes) \n- 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. \n\nContact the Scaleout team for information.\n\nSupport\n=================\n\nCommunity support is available in our `Discord\nserver <https://discord.gg/KMg4VwszAd>`__.\n\nOptions are available for `Dedicated/custom support <https://www.scaleoutsystems.com/start#pricing>`__.\n\nMaking contributions\n====================\n\nAll pull requests will be considered and are much appreciated. For\nmore details please refer to our `contribution\nguidelines <https://github.com/scaleoutsystems/fedn/blob/master/CONTRIBUTING.md>`__.\n\nCitation\n========\n\nIf you use FEDn in your research, please cite:\n\n::\n\n @article{ekmefjord2021scalable,\n title={Scalable federated machine learning with FEDn},\n 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},\n journal={arXiv preprint arXiv:2103.00148},\n year={2021}\n }\n\n\nLicense\n=======\n\nFEDn is licensed under Apache-2.0 (see `LICENSE <LICENSE>`__ file for\nfull information).\n\nUse of FEDn Studio is subject to the `Terms of Use <https://www.scaleoutsystems.com/terms>`__.\n",
"bugtrack_url": null,
"license": "Copyright 2021 Scaleout Systems AB. All rights reserved. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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