Add federated ai blog post (#48)
diff --git a/blog/2024-04-17-federated-ai.md b/blog/2024-04-17-federated-ai.md
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+---
+slug: wayang-federated-ai
+title: Wayang and the Federated AI
+authors: [glauesppen]
+tags: [wayang, federated, ai]
+---
+
+# The Federated AI
+
+AI systems and applications are widely used nowadays, from assisting grammar spellings to
+detecting early signs of cancer cells. Building an AI requires a lot of data and training to achieve
+the desired results, and federated learning is an approach to make AI training more viable.
+Federated learning (or collaborative learning) is a technique that trains AI models on data
+distributed across multiple serves or devices. It does so without centralizing data on a single
+place or storage. It also prevents the possibility of data breaches and protects sensitive
+personal data. One of the significant challenges in working with AI is the variety of tools found
+in the market or the open-source community. Each tool provides results in a different form;
+integrating them can be pretty challenging. Let's talk about Apache Wayang (incubating) and
+how it can help to solve this problem.
+
+## Apache Wayang in the Federated AI world
+
+Apache Wayang (Wayang, for short), a project in an incubation phase at Apache Software
+Foundation (ASF), integrates big data platforms and tools by removing the complexity of
+worrying about low-level details. Interestingly, even if it was not designed for, Wayang could
+also serve as a scalable platform for federated learning: the Wayang community is starting to
+work on integrating federated learning capabilities. In a federated learning approach, Wayang
+would allow different local models to be built and exchange its model results across other data
+centers to combine them into a single enhanced model.
+
+
+## A real-world example
+
+Let's consider a real-world scenario. Hospitals and health organizations have increased their
+investments in machine/deep learning initiatives to learn more and predict diagnostics.
+However, due to legal frameworks, sharing patients' information or diagnostics is impossible,
+and the solution would be to apply federated learning. To solve this problem, we could use
+Wayang to help to train the models. See the diagram 1 below:
+
+<br/>
+<img width="75%" alt="wayang stack" src="/img/architecture/federated-ai-architecture-1.png" />
+<br/><br/>
+
+As a first step, the data scientists would send an ML task to Wayang, which will work as an
+abstraction layer to connect to different data processing platforms, sparing the time to build
+integration code for each. Then, the data platforms process and generate the results that will
+be sent back to Wayang. Wayang aggregates the results into one "global result" and sends it
+back to the requestor as a next step.
+
+<br/>
+<img width="75%" alt="wayang stack" src="/img/architecture/federated-ai-architecture-2.png" />
+<br/><br/>
+
+The process repeats until the desired results are achieved.
+Although it is very much like a Federated learning pipeline, Wayang removes a considerable
+layer of complexity from the developers by integrating with diverse types of data platforms. It
+also brings fast development and reduces the need for a deep understanding of data
+infrastructure or integrations. Developers can focus on the logic and how to execute tasks
+instead of details about data processors.
+
+### Follow Wayang
+
+Apache Wayang is in an incubation phase and has a potential roadmap of implementations
+coming soon (including the federated learning aspect as well as an SQL interface and a novel
+data debugging functionality). If you want to hear or join the community, consult the link
+https://wayang.apache.org/community/ , join the mailing lists, contribute with new ideas,
+write documentation, or fix bugs.
+
+<br/>
+
+##### Thank you!
+I (Gláucia) want to thank professor Jorge Quiané for the guidance to write this blog post.
+Thanks for incentivate me to join the project and for the knowledge shared. I will always remember you.
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diff --git a/blog/authors.yml b/blog/authors.yml
index 6a26516..2bde36e 100644
--- a/blog/authors.yml
+++ b/blog/authors.yml
@@ -18,4 +18,9 @@
title: Apache Committer
url: https://github.com/juripetersen
image_url: https://avatars.githubusercontent.com/u/43411515?v=4
+glauesppen:
+ name: Gláucia Esppenchutz
+ title: (P)PMC Apache Wayang
+ url: https://github.com/glauesppen
+ image_url: https://avatars.githubusercontent.com/glauesppen
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