diff --git a/federated_learning/introduction.md b/federated_learning/introduction.md
index a6d16b9dc81ea132939afe1d90e8b024d48c3c5b..3c1199a21af4eac4b78eda8f6f692f58d42ca6c0 100644
--- a/federated_learning/introduction.md
+++ b/federated_learning/introduction.md
@@ -1,15 +1,3 @@
-## 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:
@@ -58,6 +46,20 @@ The main challenges in FL are associated to:
 
 - **Security:** adversarial attacks and data leakage.
 
+
+## 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)
+
+
 ---
 
 ## References
diff --git a/heterogeneous_data/introduction.md b/heterogeneous_data/introduction.md
index 6cc9a52c845ff9d92c63b7fe4c4a57ca41016103..4176ec36fb0693f29cc3363237f34cd31da20cf6 100644
--- a/heterogeneous_data/introduction.md
+++ b/heterogeneous_data/introduction.md
@@ -2,7 +2,7 @@
 
 This lecture aims at covering the statistical background required to perform association analysis in typical studies of heterogeneous information. We will introduce the notion of statistical association, and highlight the standard analysis paradigm in univariate modeling. We will then explore multivariate association models, generalizing to high-dimensional data the notion of statistical association. In particular, we will focus on standard paradigms such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Reduced Rank Regression (RRR). We will finally introduce more advanced analysis frameworks, such as Bayesian and deep association methods. Within this context we will present the Multi-Channel Variational Autoencoder, recently developed by our group. 
 
-Links:
+## Links:
 
 - [Presentation material](https://marcolorenzi.github.io/material/AI4Health_winter_school_part1.pdf).