From 5ba9f64585359ec8b6e76514ab5d19a0f72ec476 Mon Sep 17 00:00:00 2001 From: Teodor Parvanov Date: Fri, 15 Nov 2024 16:36:27 +0100 Subject: [PATCH] Updating the main README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index aba069a8678..e8fcac8ca99 100644 --- a/README.md +++ b/README.md @@ -37,14 +37,14 @@ For more installation options check out the [online documentation](https://openf OpenFL enables data scientists to set up a federated learning experiment following one of the workflows: -- [Aggregator-based Workflow](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html): +- [Task Runner API](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html): 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)): +- [Workflow API](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/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/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/301_MNIST_Watermarking.ipynb). The quickest way to test OpenFL is to follow our [tutorials](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials).
-Read the [blog post](https://towardsdatascience.com/go-federated-with-openfl-8bc145a5ead1) explaining steps to train a model with OpenFL.
+Read the [blog post](https://medium.com/openfl/from-centralized-machine-learning-to-federated-learning-with-openfl-b3e61da52432) explaining steps to train a model with OpenFL.
Check out the [online documentation](https://openfl.readthedocs.io/en/latest/index.html) to launch your first federation. @@ -53,7 +53,7 @@ Check out the [online documentation](https://openfl.readthedocs.io/en/latest/ind - Ubuntu Linux 18.04+ - Python 3.7+ (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. +OpenFL supports training with TensorFlow 2+ or PyTorch 2+ which should be installed separately. Users can extend the list of supported Machine Learning frameworks if needed. ## Project Overview ### What is Federated Learning