doc: new article 'How Federated Learning Protects Privacy' from PAIR …

…google

README.md

@@ -81,8 +81,12 @@ Please find below all the contributed resources, organised by category
 - [FHE.org Resources](https://fhe.org/resources/) - Compiled resources on homomorphic encryption
 
 - [Privacy-Preserving Retrieval Augmented Generation with Differential Privacy](https://arxiv.org/abs/2412.04697) - The first paper to explore RAG (Retrieval Augmented Generation) with Differential Privacy.
+
 - [Federated Learning on Non-IID Data Silos: An Experimental Study](https://arxiv.org/pdf/2102.02079v4) - This study introduces the first comprehensive benchmark with diverse data partitioning strategies to systematically evaluate FL algorithms under non-IID settings, providing valuable insights for future research. Source code: [here](https://github.com/Xtra-Computing/NIID-Bench).
 
+- [How Federated Learning Protects Privacy](https://pair.withgoogle.com/explorables/federated-learning/) - The PAIR (People + AI Research) team at Google has published this engaging article that explains how Federated Learning protects privacy. It features clear visuals and GIFs to help you better understand the concept and its applications in real-world scenarios.
+
+
 ### Courses
 
 - [The Private AI Series](https://courses.openmined.org/) - Learn how privacy technology is changing our world and how you can lead the charge.