Update index.md

index.md

@@ -23,7 +23,10 @@ Federated learning (FL) has emerged as a pivotal approach in machine learning, e
 ## ShapFed Main Algorithm
 ![Pseudocode](docs/pseudo.png)
 
-<p align="justify">**Weighted Aggregation:** The optimal weights $w_s^{\star}$ are derived using Equation 7 from the paper, while $w_s$ represents the result of applying equal weights (FedAvg). **Personalization:** Rather than distributing a uniform global model to all users, we provide personalized weights $\bar{w}_i$, which are $\gamma_i$ combinations of individual user weights $w_i$ and the optimally aggregated weight $w_s^{\star}$.</p>
+<p style="text-align: justify;">
+  <strong>Weighted Aggregation:</strong> The optimal weights <i>w<sub>s</sub>^*</i> are derived using Equation 7 from the paper, while <i>w<sub>s</sub></i> represents the result of applying equal weights (FedAvg). 
+  <strong>Personalization:</strong> Rather than distributing a uniform global model to all users, we provide personalized weights <i>w̄<sub>i</sub></i>, which are <i>γ<sub>i</sub></i> combinations of individual user weights <i>w<sub>i</sub></i> and the optimally aggregated weight <i>w<sub>s</sub>^*</i>.
+</p>
 
 ![Weighted ShapFed and Personalized ShapFed](docs/alignment.png) 
 
@@ -178,10 +181,10 @@ Federated learning (FL) has emerged as a pivotal approach in machine learning, e
     </tbody>
 </table>
 
-
+<p> </p>
 
 <table border="1" style="width:87.5%; border-collapse: collapse; margin: auto;">
-    <caption>2. Performance and fairness comparison using Pearson's correlation (↑) as a fairness metric on Fed-ISIC2019. The red highlight indicates a negative gain from collaboration.</caption>
+    <caption>Performance and fairness comparison using Pearson's correlation (↑) as a fairness metric on Fed-ISIC2019. The red highlight indicates a negative gain from collaboration.</caption>
     <thead>
         <tr style="background-color: lightblue;">
             <th>Setting</th>