Restructure framework explainers

doc/source/conf.py

@@ -119,6 +119,10 @@
     "logging": "how-to-configure-logging.html",
     "ssl-enabled-connections": "how-to-enable-ssl-connections.html",
     "upgrade-to-flower-1.0": "how-to-upgrade-to-flower-1.0.html",
+
+    # Restructuring: explanations
+    "evaluation": "explanation-federated-evaluation.html",
+    "differential-privacy-wrappers": "explanation-differential-privacy.html",
 
     # Deleted pages
     "people": "index.html",

doc/source/differential-privacy-wrappers.rst → doc/source/explanation-differential-privacy.rst

@@ -1,5 +1,5 @@
-Differential Privacy Wrappers in Flower
-=======================================
+Differential privacy
+====================
 
 Flower provides differential privacy (DP) wrapper classes for the easy integration of the central DP guarantees provided by DP-FedAvg into training pipelines defined in any of the various ML frameworks that Flower is compatible with. 
 

doc/source/evaluation.rst → doc/source/explanation-federated-evaluation.rst

@@ -1,8 +1,9 @@
-Evaluation
-==========
+Federated evaluation
+====================
 
 There are two main approaches to evaluating models in federated learning systems: centralized (or server-side) evaluation and federated (or client-side) evaluation.
 
+
 Centralized Evaluation
 ----------------------
 
@@ -171,6 +172,7 @@ Model parameters can also be evaluated during training. :code:`Client.fit` can r
         def evaluate(self, parameters, config):
             # ...
 
+
 Full Code Example
 -----------------
 

doc/source/index.rst

@@ -109,8 +109,8 @@ Understanding-oriented concept guides explain and discuss key topics and underly
    :maxdepth: 1
    :caption: Explanations
 
-   evaluation
-   differential-privacy-wrappers
+   explanation-federated-evaluation
+   explanation-differential-privacy
 
 References
 ~~~~~~~~~~