Unverified Commit 00a56e6f authored by Andrews Cordolino Sobral's avatar Andrews Cordolino Sobral Committed by GitHub
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[federated-learning] Added documentation for the federated-learning bucket (#781)

* [federated-learning] Added documentation for the federated-learning bucket

* fixed typos
parent a1524570
......@@ -410,6 +410,50 @@ The following workflows represent python templates that can be used to implement
*R_Task:* is a simple R task template pre-configured to run with `Distributed_Auto_ML`.
[[_FL]]
== Federated Learning (FL)
https://ai.googleblog.com/2017/04/federated-learning-collaborative.html[Federated Learning (FL)] enables to train an algorithm across multiple decentralized devices (or servers) holding local data samples, without exchanging them.
The `federated-learning` bucket contains a few examples of Federated Learning workflows that can be easily used to build a common and robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights and access to heterogeneous data.
This bucket uses the https://flower.dev/[Flower] library to implement federated learning workflows.
The https://flower.dev/[Flower] library is a friendly federated learning framework that presents a unified approach for federated learning.
It help federating any workload using any ML framework, and any programming language.
image::FlowerArchitecture.png[align=center]
=== PyTorch Federated Learning Tasks
The following workflows represent a client/server templates that can be used to implement a Federated Learning workflow using PyTorch.
*PyTorch_FL_Client_Task:* is a Federated Learning Client task template using PyTorch.
*PyTorch_FL_Server_Task:* is a Federated Learning Server task template using PyTorch.
=== TensorFlow Federated Learning Tasks
The following workflows represent a client/server templates that can be used to implement a Federated Learning workflow using TensorFlow/Keras.
*TensorFlow_FL_Client_Task:* is a Federated Learning Client task template using TensorFlow/Keras.
*TensorFlow_FL_Server_Task:* is a Federated Learning Server task template using TensorFlow/Keras.
=== Federated Learning Workflows
The following workflows uses the federated learning to train a deep Convolutional Neural Network (ConvNet/CNN) on the https://www.cs.toronto.edu/~kriz/cifar.html[CIFAR10 images dataset] using the https://flower.dev/[Flower] library.
*PyTorch_Federated_Learning_Example:* shows an example of Federated Learning workflow using PyTorch.
*TensorFlow_Federated_Learning_Example:* shows an example of Federated Learning workflow using TensorFlow/Keras.
References:
1. http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf[Communication-Efficient Learning of Deep Networks from Decentralized Data]
2. https://arxiv.org/pdf/2007.14390.pdf[Flower: A Friendly Federated Learning Research Framework]
== Model as a Service for Machine Learning (MaaS_ML)
Once a predictive model is built, tested and validated, you can easily use it in real world production pipelines by deploying it as a REST Web Service via the MaaS_ML service.
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