Update content/patterns/multicloud-federated-learning/_index.adoc

Co-authored-by: Avani Bhatt <[email protected]>

Author: yanmxa

content/patterns/multicloud-federated-learning/_index.adoc

@@ -26,7 +26,7 @@ include::modules/comm-attributes.adoc[]
 
 As machine learning (ML) evolves, protecting data privacy becomes increasingly important. Since ML depends on large volumes of data, it is essential to secure that data without disrupting the learning process.
 
-Federated Learning (FL) addresses this by allowing multiple clusters or organizations to collaboratively train models without sharing sensitive data. Computation happens where the data lives, ensuring privacy, regulatory compliance, and efficiency.
+Federated learning addresses this by allowing multiple clusters or organizations to collaboratively train models without sharing sensitive data. Computation happens where the data lives, ensuring privacy, regulatory compliance, and efficiency.
 
 By integrating federated learning  with {rh-rhacm-first}, this pattern provides an automated and scalable solution for deploying FL workloads across hybrid and multicluster environments.