Update introduction.md

federated_learning/introduction.md

+## Links
+
+[Presentation material](https://ecaad164-c957-4008-a451-5e1098ff8953.filesusr.com/ugd/68a50d_a3d074241b3a4342be2fef2413ee61c7.pdf)
+[Colab notebook - part 1](https://colab.research.google.com/drive/1_uemRwNuok1wop6wP2Aiokn0KQgcwfr1?usp=sharing)
+[Colab notebook - part 2](https://colab.research.google.com/drive/1PiUee4n8T7pIhDV5zDEqhsK5jXvDYHpO?usp=sharing)
+[Colab notebook - part 3](https://colab.research.google.com/drive/1kIbrUtNH_WIPQX5vLyzRjs5CTgKA2CMT?usp=sharing)
+[Colab notebook - part 4](https://colab.research.google.com/drive/10wEN9eqdE9Z7CtvhRFgsL3gAzunZGlee?usp=sharing)
+
 # Introduction
 
 Standard machine learning approaches require to have a centralizaed dataset in order to train a model. In certain scenarios like in the biomedical field, this is not straightforward due to several reasons like:
@@ -56,6 +64,5 @@ The main challenges in FL are associated to:
 
 3. **Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020).** *Federated learning: Challenges, methods, and future directions*. IEEE Signal Processing Magazine, 37(3), 50-60.
 
-## Link
 
-[Presentation material](https://ecaad164-c957-4008-a451-5e1098ff8953.filesusr.com/ugd/68a50d_a3d074241b3a4342be2fef2413ee61c7.pdf)
+