Metadata-Version: 2.1
Name: openfl
Version: 1.2
Summary: Federated Learning for the Edge
Home-page: https://github.com/intel/openfl
Author: Intel Corporation
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/intel/openfl/issues
Project-URL: Documentation, https://openfl.readthedocs.io/en/stable/
Project-URL: Source Code, https://github.com/intel/openfl
Description: 
        # Welcome to Intel&reg; Open Federated Learning
        
        [![PyPI - Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue)](https://pypi.org/project/openfl/)
        [![Jenkins](https://img.shields.io/jenkins/build?jobUrl=http%3A%2F%2F213.221.44.203%2Fjob%2FFederated-Learning%2Fjob%2Fnightly%2F)](http://213.221.44.203/job/Federated-Learning/job/nightly/)
        [![Documentation Status](https://readthedocs.org/projects/openfl/badge/?version=latest)](https://openfl.readthedocs.io/en/latest/?badge=latest)
        [![Downloads](https://pepy.tech/badge/openfl)](https://pepy.tech/project/openfl)
        [![PyPI version](https://img.shields.io/pypi/v/openfl)](https://pypi.org/project/openfl/)
        [<img src="https://img.shields.io/badge/slack-@openfl-blue.svg?logo=slack">](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw) 
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        [![Citation](https://img.shields.io/badge/cite-citation-blue)](https://arxiv.org/abs/2105.06413)
        
        
        [Federated learning](https://en.wikipedia.org/wiki/Federated_learning) is a distributed machine learning approach that
        enables organizations to collaborate on machine learning projects
        without sharing sensitive data, such as, patient records, financial data,
        or classified secrets 
        ([Sheller MJ,  et al., 2020](https://www.nature.com/articles/s41598-020-69250-1);
        [Sheller MJ, et al., 2019](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589345);
        [Yang Y, et al., 2019](https://arxiv.org/abs/1902.04885);
        [McMahan HB, et al., 2016](https://arxiv.org/abs/1602.05629)).
        
        
        The basic premise behind federated learning
        is that the model moves to meet the data rather than the data moving
        to meet the model. Therefore, the minimum data movement needed
        across the federation is solely the model parameters and their updates.
        
        
        Open Federated Learning (OpenFL) is a Python 3 project developed by Intel Labs and 
        Intel Internet of Things Group. 
        
        ![Federated Learning](https://raw.githubusercontent.com/intel/openfl/master/docs/images/diagram_fl.png)
        
        ## Getting started
        
        Check out our [online documentation](https://openfl.readthedocs.io/en/latest/index.html) to launch your first federation.  The quickest way to test OpenFL is through our [Jupyter Notebook tutorials](https://openfl.readthedocs.io/en/latest/running_the_federation.notebook.html).
        
        For more questions, please consider joining our [Slack channel](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw).
        
        
        ## Requirements
        
        - OS: Tested on Ubuntu Linux 16.04 and 18.04.
        - Python 3.6+ with a Python virtual environment (e.g. [conda](https://docs.conda.io/en/latest/), recommended version: 4.9 and above)
        - TensorFlow 2+ or PyTorch 1.6+ (depending on your training requirements). OpenFL is designed to easily support other frameworks as well.
        
        ![fx commandline interface](https://raw.githubusercontent.com/intel/openfl/master/docs/images/fx_help.png)
        
        ## License
        This project is licensed under [Apache License Version 2.0](LICENSE).
        By contributing to the project, you agree to the license and copyright terms therein
        and release your contribution under these terms.
        
        ## Resources:
        * Docs and Tutorials: https://openfl.readthedocs.io/en/latest/index.html
        * Issue tracking: https://github.com/intel/openfl/issues
        * [Slack channel](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw)
        
        ## Citation
        
        ```
        @misc{reina2021openfl,
              title={OpenFL: An open-source framework for Federated Learning}, 
              author={G Anthony Reina and Alexey Gruzdev and Patrick Foley and Olga Perepelkina and Mansi Sharma and Igor Davidyuk and Ilya Trushkin and Maksim Radionov and Aleksandr Mokrov and Dmitry Agapov and Jason Martin and Brandon Edwards and Micah J. Sheller and Sarthak Pati and Prakash Narayana Moorthy and Shih-han Wang and Prashant Shah and Spyridon Bakas},
              year={2021},
              eprint={2105.06413},
              archivePrefix={arXiv},
              primaryClass={cs.LG}
        }
        ```
        
        ## Support
        Please report questions, issues and suggestions using:
        
        * [GitHub* Issues](https://github.com/intel/openfl/issues)
        * [Slack channel](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw)
        
        ### Relation to OpenFederatedLearning and the Federated Tumor Segmentation (FeTS) Initiative
        
        This project builds on the [Open Federated Learning](https://github.com/IntelLabs/OpenFederatedLearning) framework that was 
        developed as part of a collaboration between Intel
        and the University of Pennsylvania (UPenn) for federated learning. 
        It describes Intel’s commitment in 
        supporting the grant awarded to the [Center for Biomedical Image Computing and Analytics (CBICA)](https://www.cbica.upenn.edu/) 
        at UPenn (PI: S. Bakas) from the [Informatics Technology for Cancer Research (ITCR)](https://itcr.cancer.gov/) program of 
        the National Cancer Institute (NCI) of the National Institutes of Health (NIH), 
        for the development of the [Federated Tumor Segmentation (FeTS, www.fets.ai)](https://www.fets.ai/) 
        platform (grant award number: U01-CA242871). 
        
        FeTS is an exciting, real-world 
        medical FL platform, and we are honored to be collaborating with UPenn in 
        leading a federation of international collaborators. The original OpenFederatedLearning
        project and OpenFL are designed to serve as the backend for the FeTS platform, 
        and OpenFL developers and researchers continue to work very closely with UPenn on 
        the FeTS project. The 
        [FeTS-AI/Front-End](https://github.com/FETS-AI/Front-End) shows how UPenn 
        and Intel have integrated UPenn’s medical AI expertise with Intel’s framework 
        to create a federated learning solution for medical imaging. 
        
        Although initially developed for use in medical imaging, this project is
        designed to be agnostic to the use-case, the industry, and the 
        machine learning framework.
        
        
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: System :: Distributed Computing
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6, <3.9
Description-Content-Type: text/markdown
