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docs/about.html

@@ -115,15 +115,12 @@ <h1><a href="index.html">PFLlib</a></h1>
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             <h2>About PFLlib</h2>
-            <p>PFLlib is a powerful library designed for privacy-preserving federated learning. It allows multiple parties to collaboratively train machine learning models without sharing their raw data, ensuring both security and efficiency.</p>
-            <h3>Key Features</h3>
-            <ul>
-                <li><strong>Privacy-Preserving:</strong> Protects sensitive data while enabling collaborative learning.</li>
-                <li><strong>Scalable:</strong> Efficiently handles large-scale federated learning tasks.</li>
-                <li><strong>Easy-to-Use:</strong> Provides a straightforward API for seamless integration and usage.</li>
-            </ul>
-            <h3>Why Choose PFLlib?</h3>
-            <p>PFLlib is ideal for organizations looking to leverage federated learning techniques while maintaining strict data privacy standards. Its robust features make it suitable for various applications ranging from healthcare to finance.</p>
+            <h3>Mission</h3>
+            <p>We create a <em>beginner-friendly</em> library with a evaluation platform for those new to federated learning (FL). <a href="https://github.com/TsingZ0/PFLlib/pulls"><strong>Join us</strong></a> in benefiting the FL community by contributing your algorithms, datasets, and metrics to this project.</p>
+            <h3>Value</h3>
+            <p>PFLlib is ideal for companies aiming to explore, select, and evaluate personalized federated learning methods. It enables the evaluation of algorithms and their adaptability to diverse scenarios, offering valuable insights for informed algorithm selection in real-world applications. With its robust features, PFLlib is well-suited for a wide range of industries, from healthcare to finance.</p>
+            <h2>About Me</h2>
+
         </section>
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docs/docs.html

@@ -192,10 +192,8 @@ <h2>Introduction</h2>
 
                 <p>The origin of the <strong>data heterogeneity</strong> phenomenon is the characteristics of users, who generate non-IID (not Independent and Identically Distributed) and unbalanced data. With data heterogeneity existing in the FL scenario, a myriad of approaches have been proposed to crack this hard nut. In contrast, the personalized FL (pFL) may take advantage of the statistically heterogeneous data to learn the personalized model for each user.</p>
 
-                <h4>Our Mission</h4>
-                <p>We create a <em>beginner-friendly</em> library with a evaluation platform for those new to federated learning (FL). <a href="https://github.com/TsingZ0/PFLlib/pulls"><strong>Join us</strong></a> in benefiting the FL community by contributing your algorithms, datasets, and metrics to this project.</p>
 
-                <h4>Streamlined File Structure</h4>
+                <h4>Simple File Structure</h4>
                 <img src="imgs/structure.png" alt="PFLlib File Structure" width=100%>
                 <p>An Example for FedAvg. You can create a scenario using <code>generate_DATA.py</code> and run an algorithm using <code>main.py</code>, <code>clientNAME.py</code>, and <code>serverNAME.py</code>. For a new algorithm, you only need to add new features in <code>clientNAME.py</code> and <code>serverNAME.py</code>.</p>
 

docs/index.html

@@ -212,7 +212,7 @@ <h2>Scalable</h2>
         <div class="feature-item">
             <img src="imgs/easy-to-use.svg" alt="Easy-to-Use">
             <h2>Easy-to-Use</h2>
-            <p>Streamlined file structure, encapsulated function interface, with each FL algorithm requiring just two files.</p>
+            <p>Simple file structure, encapsulated function interface, with each FL algorithm requiring just two files.</p>
         </div>
     </div>
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