Merge pull request #1001 from yanmxa/br_doc_update

Correct the url in the installation doc

CONTRIBUTING.md

@@ -6,7 +6,7 @@ We welcome contributions from the community. There are several ways to contribut
 * Answering questions on our [discussions page](https://github.com/securefederatedai/openfl/discussions).
 * Participating in our [roadmap](https://github.com/securefederatedai/openfl/blob/develop/ROADMAP.md) discussions.
 
-We have a slack [channel](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw) and we host regular [community meetings](https://github.com/intel/openfl#support).
+We have a slack [channel](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw) and we host regular [community meetings](https://github.com/securefederatedai/openfl#support).
 
 
 ## How to contribute code
@@ -35,7 +35,7 @@ commits using `git commit --signoff`.
 
 Your signature [certifies](http://developercertificate.org/) that you wrote the patch, or, you otherwise have the right to pass it on as an open-source patch.
 
-OpenFL is licensed under the [Apache 2.0 license](https://github.com/intel/openfl/blob/develop/LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
+OpenFL is licensed under the [Apache 2.0 license](https://github.com/securefederatedai/openfl/blob/develop/LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
 
 ### Step 5. Code review and merge
 

README.md

@@ -3,8 +3,8 @@
 </div>
 
 [![PyPI - Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)](https://pypi.org/project/openfl/)
-[![Ubuntu CI status](https://github.com/intel/openfl/actions/workflows/ubuntu.yml/badge.svg)](https://github.com/intel/openfl/actions/workflows/ubuntu.yml)
-[![Windows CI status](https://github.com/intel/openfl/actions/workflows/windows.yml/badge.svg)](https://github.com/intel/openfl/actions/workflows/windows.yml)
+[![Ubuntu CI status](https://github.com/securefederatedai/openfl/actions/workflows/ubuntu.yml/badge.svg)](https://github.com/securefederatedai/openfl/actions/workflows/ubuntu.yml)
+[![Windows CI status](https://github.com/securefederatedai/openfl/actions/workflows/windows.yml/badge.svg)](https://github.com/securefederatedai/openfl/actions/workflows/windows.yml)
 [![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)
 [![DockerHub](https://img.shields.io/docker/pulls/intel/openfl.svg)](https://hub.docker.com/r/intel/openfl)
@@ -44,9 +44,9 @@ Setup long-lived components to run many experiments in series. Recommended for F
 Define an experiment and distribute it manually. All participants can verify model code and [FL plan](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html#federated-learning-plan-fl-plan-settings) prior to execution. The federation is terminated when the experiment is finished
 
 - [Workflow Interface](https://openfl.readthedocs.io/en/latest/about/features_index/workflowinterface.html) ([*experimental*](https://openfl.readthedocs.io/en/latest/developer_guide/experimental_features.html)):
-Create complex experiments that extend beyond traditional horizontal federated learning. See the [experimental tutorials](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/) to learn how to coordinate [aggregator validation after collaborator model training](https://github.com/intel/openfl/tree/develop/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb), [perform global differentially private federated learning](https://github.com/psfoley/openfl/tree/experimental-workflow-interface/openfl-tutorials/experimental/Global_DP), measure the amount of private information embedded in a model after collaborator training with [privacy meter](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/Privacy_Meter/readme.md), or [add a watermark to a federated model](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb).
+Create complex experiments that extend beyond traditional horizontal federated learning. See the [experimental tutorials](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/) to learn how to coordinate [aggregator validation after collaborator model training](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb), [perform global differentially private federated learning](https://github.com/psfoley/openfl/tree/experimental-workflow-interface/openfl-tutorials/experimental/Global_DP), measure the amount of private information embedded in a model after collaborator training with [privacy meter](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Privacy_Meter/readme.md), or [add a watermark to a federated model](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb).
 
-The quickest way to test OpenFL is to follow our [tutorials](https://github.com/intel/openfl/tree/develop/openfl-tutorials). </br>
+The quickest way to test OpenFL is to follow our [tutorials](https://github.com/securefederatedai/openfl/tree/develop/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.
 
@@ -106,7 +106,7 @@ See you there!
 
 We also always welcome questions, issue reports, and suggestions via:
 
-* [GitHub Issues](https://github.com/intel/openfl/issues)
+* [GitHub Issues](https://github.com/securefederatedai/openfl/issues)
 * [Slack workspace](https://join.slack.com/t/openfl/shared_invite/zt-ovzbohvn-T5fApk05~YS_iZhjJ5yaTw)
 
 ## License

ROADMAP.md

@@ -15,7 +15,7 @@ All interfaces in OpenFL support the standard horizontal FL training workflow to
 4. The collaborator performs validation with their local validation dataset on their locally trained model, and sends their validation metrics to the aggregator (locally_tuned_model_validation task)
 5. The aggregator applies an aggregation function (weighted average, FedCurv, FedProx, etc.) to the model weights, and reports the aggregate metrics.
 
-The [Task Assigner](https://github.com/intel/openfl/blob/develop/openfl-workspace/workspace/plan/defaults/assigner.yaml#L7-L9) determines the list of collaborator tasks to be performed, 
+The [Task Assigner](https://github.com/securefederatedai/openfl/blob/develop/openfl-workspace/workspace/plan/defaults/assigner.yaml#L7-L9) determines the list of collaborator tasks to be performed, 
 and both in the task runner API as well as the interactive API these tasks can be modified (to varying degrees).
 For example, to perform federated evaluation of a model, only the `aggregated_model_validation` task would be listed for the assigner's block of the federated plan.
 Equivalently for the interactive API, this can be done by only registering a single validation task.
@@ -36,7 +36,7 @@ In the process of thinking about federated workflows, and the properties that ar
 7. Don't reinvent unless absolutely necessary
 
 ### 1.2 Security, Privacy, and Governance
-OpenFL is designed for security and privacy, and soon we will be releasing some exciting extensions that build on running [OpenFL experiments within SGX enclaves](https://github.com/intel/openfl/blob/develop/openfl-gramine/MANUAL.md).   
+OpenFL is designed for security and privacy, and soon we will be releasing some exciting extensions that build on running [OpenFL experiments within SGX enclaves](https://github.com/securefederatedai/openfl/blob/develop/openfl-gramine/MANUAL.md).   
 
 ### 1.3 Decoupling interface from infrastructure
 The task runner interface is coupled with the the single experiment aggregator / collaborator infrastructure, and the interactive API is tied to the director / envoy infrastructure. 

docs/about/features_index/taskrunner.rst

@@ -42,7 +42,7 @@ Configurable Settings
 ^^^^^^^^^^^^^^^^^^^^^
 
 - :class:`Aggregator <openfl.component.Aggregator>`
-    `openfl.component.Aggregator <https://github.com/intel/openfl/blob/develop/openfl/component/aggregator/aggregator.py>`_
+    `openfl.component.Aggregator <https://github.com/securefederatedai/openfl/blob/develop/openfl/component/aggregator/aggregator.py>`_
     Defines the settings for the aggregator which is the model-owner in the experiment. While models can be trained from scratch, in many cases the federation performs fine-tuning of a previously trained model. For this reason, pre-trained weights for the model are stored in protobuf files on the aggregator node and passed to collaborator nodes during initialization. The settings for aggregator include:
 
  - :code:`init_state_path`: (str:path) Defines the weight protobuf file path where the experiment's initial weights will be loaded from. These weights will be generated with the `fx plan initialize` command.
@@ -53,15 +53,15 @@ Configurable Settings
 
 
 - :class:`Collaborator <openfl.component.Collaborator>`
-    `openfl.component.Collaborator <https://github.com/intel/openfl/blob/develop/openfl/component/collaborator/collaborator.py>`_
+    `openfl.component.Collaborator <https://github.com/securefederatedai/openfl/blob/develop/openfl/component/collaborator/collaborator.py>`_
     Defines the settings for the collaborator which is the data owner in the experiment. The settings for collaborator include:
 
  - :code:`delta_updates`: (boolean) Determines whether the difference in model weights between the current and previous round will be sent (True), or if whole checkpoints will be sent (False). Setting to delta_updates to True leads to higher sparsity in model weights sent across, which may improve compression ratios.
  - :code:`opt_treatment`: (str) Defines the optimizer state treatment policy. Valid options are : 'RESET' - reinitialize optimizer for every round (default), 'CONTINUE_LOCAL' - keep local optimizer state for every round, 'CONTINUE_GLOBAL' - aggregate optimizer state for every round.
 
 
 - :class:`Data Loader <openfl.federated.data.loader.DataLoader>`
-    `openfl.federated.data.loader.DataLoader <https://github.com/intel/openfl/blob/develop/openfl/federated/data/loader.py>`_
+    `openfl.federated.data.loader.DataLoader <https://github.com/securefederatedai/openfl/blob/develop/openfl/federated/data/loader.py>`_
     Defines the data loader class that provides access to local dataset. It implements a train loader and a validation loader that takes in the train dataset and the validation dataset respectively. The settings for the dataloader include:
 
  - :code:`collaborator_count`: (int) The number of collaborators participating in the federation
@@ -70,12 +70,12 @@ Configurable Settings
 
 
 - :class:`Task Runner <openfl.federated.task.runner.TaskRunner>`
-    `openfl.federated.task.runner.TaskRunner <https://github.com/intel/openfl/blob/develop/openfl/federated/task/runner.py>`_
+    `openfl.federated.task.runner.TaskRunner <https://github.com/securefederatedai/openfl/blob/develop/openfl/federated/task/runner.py>`_
     Defines the model, training/validation functions, and how to extract and set the tensors from model weights and optimizer dictionary. Depending on different AI frameworks like PyTorch and Tensorflow, users can select pre-defined task runner methods.
 
 
 - :class:`Assigner <openfl.component.Assigner>`
-    `openfl.component.Assigner <https://github.com/intel/openfl/blob/develop/openfl/component/assigner/assigner.py>`_
+    `openfl.component.Assigner <https://github.com/securefederatedai/openfl/blob/develop/openfl/component/assigner/assigner.py>`_
     Defines the task that are sent to the collaborators from the aggregator. There are three default tasks that could be given to each Collaborator:
 
  - :code:`aggregated_model_validation`: (str) Perform validation on aggregated global model sent by the aggregator.
@@ -106,7 +106,7 @@ Each task subsection contains the following:
 - ``kwargs``: kwargs passed to the ``function``.
 
 .. note::
-    See an `example <https://github.com/intel/openfl/blob/develop/openfl/federated/task/runner.py>`_ of the :class:`TaskRunner <openfl.federated.TaskRunner>` class for details.
+    See an `example <https://github.com/securefederatedai/openfl/blob/develop/openfl/federated/task/runner.py>`_ of the :class:`TaskRunner <openfl.federated.TaskRunner>` class for details.
 
 
 .. _running_the_federation_manual:
@@ -525,7 +525,7 @@ Option 1: Deploy a Federation in a Docker Container
        $ docker run -it --network host openfl
 
 
-You can now experiment with |productName| in the container. For example, you can test the project pipeline with the `"Hello Federation" bash script <https://github.com/intel/openfl/blob/develop/tests/github/test_hello_federation.sh>`_.
+You can now experiment with |productName| in the container. For example, you can test the project pipeline with the `"Hello Federation" bash script <https://github.com/securefederatedai/openfl/blob/develop/tests/github/test_hello_federation.sh>`_.
 
 
 .. _running_the_federation_docker_workspace: