Docs correction

README.md

@@ -1,5 +1,4 @@
-
-# Welcome to Intel® Open Federated Learning
+# OpenFL - An Open-Source Framework For 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/)
@@ -10,51 +9,75 @@
 [![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)
 
+OpenFL is a Python 3 framework for Federated Learning. OpenFL is designed to be a _flexible_, _extensible_ and _easily learnable_ tool for data scientists. OpenFL is developed by Intel Internet of Things Group (IOTG) and Intel Labs. 
 
-[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)).
-
+## Installation
 
-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.
+You can simply install OpenFL from PyPI:
 
+```
+$ pip install openfl
+```
+For more installation options check out the [online documentation](https://openfl.readthedocs.io/en/latest/install.html).
 
-Open Federated Learning (OpenFL) is a Python 3 project developed by Intel Internet of Things Group (IOTG) and Intel Labs. 
+## Getting Started
 
-![Federated Learning](https://raw.githubusercontent.com/intel/openfl/master/docs/images/diagram_fl.png)
 
-## Getting started
+OpenFL enables data scientists to set up a federated learning experiment following one of the workflows:
 
-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://github.com/intel/openfl/tree/develop/openfl-tutorials).
+- [Director-based Workflow](https://openfl.readthedocs.io/en/latest/source/workflow/director_based_workflow.html) [preferred]:
+a federation created with this workflow continues to be available to distribute more experiments in series.
 
-For more questions, please consider joining our [Slack channel](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw).
+- [Aggregator-based Workflow](https://openfl.readthedocs.io/en/latest/source/workflow/running_the_federation.agg_based.html):
+with this workflow, the federation is terminated when the experiment is finished.
 
+The quickest way to test OpenFL is to follow our [tutorials](https://github.com/intel/openfl/tree/docs_correction/openfl-tutorials). </br>
+Read the [blog post](https://towardsdatascience.com/go-federated-with-openfl-8bc145a5ead1) explaining steps to train a model with OpenFL. </br>
+Check out the [online documentation](https://openfl.readthedocs.io/en/latest/index.html) to launch your first federation.
 
 ## 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.
+- Ubuntu Linux 16.04 or 18.04.
+- Python 3.6+ (recommended to use with [Virtualenv](https://virtualenv.pypa.io/en/latest/)).
+
+OpenFL supports training with TensorFlow 2+ or PyTorch 1.3+ which should be installed separately. User can extend the list of supported Deep Learning frameworks if needed.
+
+## Project Overview
+### What is Federated Learning
+
+[Federated learning](https://en.wikipedia.org/wiki/Federated_learning) is a distributed machine learning approach that enables collaboration on machine learning projects without having to share sensitive data, such as, patient records, financial data, or classified information. The minimum data movement needed across the federation is solely the model parameters and their updates.
+
+![Federated Learning](https://github.com/intel/openfl/blob/docs_correction/docs/images/diagram_fl_new.png?raw=true)
+
+
+### Background
+OpenFL builds on the [OpenFederatedLearning](https://github.com/IntelLabs/OpenFederatedLearning) framework, which was a collaboration between Intel and the University of Pennsylvania (UPenn) to develop the [Federated Tumor Segmentation (FeTS, www.fets.ai)](https://www.fets.ai/) platform (grant award number: U01-CA242871). 
+
+The grant for FeTS was 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). 
+
+FeTS is a real-world medical federated learning platform with 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. An example is the [FeTS-AI/Front-End](https://github.com/FETS-AI/Front-End), which integrates 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, OpenFL designed to be agnostic to the use-case, the industry, and the machine learning framework.
+
+You can find more details in the following articles:
+- [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)
+
+
+
+## Support
+We welcome questions, issue reports, and suggestions:
+
+* [GitHub Issues](https://github.com/intel/openfl/issues)
+* [Slack workspace](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw)
 
-![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.
+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
 
@@ -69,35 +92,3 @@ and release your contribution under these terms.
 }
 ```
 
-## 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.
-

docs/Makefile

@@ -21,7 +21,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-
-	sphinx-apidoc -f -o . ../openfl
-
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/README.md

@@ -3,27 +3,21 @@
 We use sphinx to generate the documentation for this project.
 The documentation project has been initialized properly and we basically just need to update the actual content.
 
-If we ever change the code structure since last compilation, we may need to regenerate the docstring index:
-```shell
-sphinx-apidoc -f -o . ../openfl
-sphinx-apidoc -f -o . ../models
-```
-
-The command detects the code structure under `../openfl` and generates a series of `*.rst` files, such as `openfl.aggregator.rst`.
-However, the docstring would not be compiled until we execute `make html` later.
+Install requirements for building documentation:
 
-We can also update the hand-crafted documents, including `intro.rst` and `tutorial.rst`. The `index.rst` is the entry file. We don't need to modify it unless we want to add more hand-crafted pages or adjust the order in the Contents page.
+```sh
+pip install -r requirements-docs.txt
+```
 
 
-After completing revision on the .rst files, we would compile the documentation source code:
-```
+The Makefile supports many targets. We choose html because we can easily host the documentation on a remote server. Compile the documentation source code:
+```sh
 make clean
 make html
 ```
 
-The Makefile supports many targets. We choose html because we can easily host the documentation on a remote server:
-
-```shell
+Open documentation locally:
+```sh
 cd _build/html
 python -m http.server
 ```

docs/advanced_topics.rst

@@ -1,17 +1,49 @@
-.. # Copyright (C) 2020 Intel Corporation
-.. # Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.
+.. # Copyright (C) 2020-2021 Intel Corporation
+.. # SPDX-License-Identifier: Apache-2.0
 
 .. _advanced_topics:
 
 ***************
 Advanced Topics
 ***************
+**General**
+
+    Speed up activating Open Federated Learning (|productName|) commands:
+
+    - :doc:`bash_autocomplete_activation`
+
+**Aggregator-Based Workflow**
+
+    Learn to manage multiple Federation Learning plans (FL plan) in the same workspace:
+
+    - :doc:`multiple_plans`
+
+    Reduce the amount of data transferred in a federation through compression pipelines available in |productName|:
+
+    - :doc:`compression_settings`
+
+    Customize the aggregation function for each task:
+
+    - :doc:`overriding_agg_fn`
+
+**Director-Based Workflow**
+
+    Customize the logging function for each task:
+
+    - :doc:`log_metric_callback`
+
+
+
+
 
 .. toctree::
    :maxdepth: 4
+   :hidden:
 
+   bash_autocomplete_activation
    multiple_plans
    compression_settings
    overriding_agg_fn
-   bash_autocomplete_activation
    log_metric_callback
+
+

docs/bash_autocomplete_activation.rst

@@ -1,33 +1,39 @@
-.. # Copyright (C) 2021 Intel Corporation
-.. # Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.
+.. # Copyright (C) 2020-2021 Intel Corporation
+.. # SPDX-License-Identifier: Apache-2.0
 
-Bash Autocomplete Activation
-############################
+**************************
+Activate Bash Autocomplete
+**************************
 
-|productName| allows to activate bash completion in CLI mode to give faster access to all available commands.
+Get faster access to available commands by activating bash completion in CLI mode.
 
-Preparation
-~~~~~~~~~~~
+STEP 1: Preparation
+===================
 
-Make sure that you inside virtual environment with installed |productName|.
-If not use the instruction :ref:`install_initial_steps`.
+Make sure you are inside a virtual environment with Open Federated Learning (|productName|) installed. See :ref:`install_package` for details.
 
-Create ~/.fx-autocomplete.sh script
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-   This step need to be done only one time when you don't have `~/.fx-autocomplete.sh` or `~/.fx-autocomplete.sh` have corrupted content.
+STEP 2: Create the fx-autocomplete.sh Script
+============================================
+
+.. note::
+
+    Perform this procedure if you don't have a **~/.fx-autocomplete.sh** script or if the existing **~/.fx-autocomplete.sh** script is corrupted.
+
+1. Create the script.
 
    .. code-block:: console
 
-      $ _FX_COMPLETE=bash_source fx > ~/.fx-autocomplete.sh
+      _FX_COMPLETE=bash_source fx > ~/.fx-autocomplete.sh
 
-   Check that command was executed correctly.
+
+2. Check that the script was created properly.
 
    .. code-block:: console
 
-      $ cat ~/.fx-autocomplete.sh
+      cat ~/.fx-autocomplete.sh
 
-   Console output should look like example below (Click==8.0.1), but could be different depend on `Click https://click.palletsprojects.com/en/8.0.x/`_ version:
+ The output should look like the example below (Click==8.0.1), but could be different depend on `Click <https://click.palletsprojects.com/en/8.0.x/>`_ version:
 
    .. code-block:: console
 
@@ -60,22 +66,32 @@ Create ~/.fx-autocomplete.sh script
 
       _fx_completion_setup;
 
-Activate autocomplete feature
-~~~~~~~~~~~~~~~~~~~~~
 
-   This step should be done every time when you open a new terminal window.
+STEP 3: Activate the Autocomplete Feature
+=========================================
+
+Perform this command every time you open a new terminal window.
 
    .. code-block:: console
 
-      $ source ~/.fx-autocomplete.sh
+      source ~/.fx-autocomplete.sh
 
-Auto activation autocomplete
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-   To save your time you can add autocomplete activation step to `~/.bashrc`.
-
+To save time, add the script into **.bashrc** so the script is activated when you log in.
+
+1. Edit the **.bashrc** file. The **nano** command line editor is used in this example.
+
+   .. code-block:: console
+
+      nano ~/.bashrc
+
+2. Add the script.
+
    .. code-block:: bash
+
       . ~/.fx-autocomplete.sh
 
-   Save `~/.bashrc`.
-   Open new terminal to use updated `~/.bashrc`.
+3. Save your changes.
+
+4. Open a new terminal to use the updated bash shell.
+