add slogan

Author: TsingZ0

README.md

@@ -1,10 +1,6 @@
 # PFLlib: Personalized Federated Learning Library and Benchmark
 
-🎯***We create a beginner-friendly algorithm library and benchmark platform for those new to federated learning. Join us in expanding the FL community by contributing your algorithms, datasets, and metrics to this project.***
-
-👏 **PFLlib is using Apache License.**
-
-👏 **PFLlib now has its official website and domain name: https://www.pfllib.com/!!!**
+🎯***We built a beginner-friendly federated learning (FL) library and benchmark: **master FL in 2 hours—run it on your PC!** [Contribute](#easy-to-extend) your algorithms, datasets, and metrics to grow the FL community.***
 
 👏 **The **[Leaderboard](http://www.pfllib.com/benchmark.html)** is live! Our methods—[FedCP](https://github.com/TsingZ0/FedCP), [GPFL](https://github.com/TsingZ0/GPFL), and [FedDBE](https://github.com/TsingZ0/DBE)—lead the way. Notably, **FedDBE** stands out with robust performance across varying data heterogeneity levels.**
 

docs/about.html

@@ -159,7 +159,7 @@ <h1><a href="index.html">PFLlib</a></h1>
         <section id="about">
             <h2>About PFLlib</h2>
             <h3>Mission</h3>
-            <p>We create a <strong><em>beginner-friendly</em> library with a benchmark platform</strong> 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>
+            <p>We create a <strong><em>beginner-friendly</em> library with a benchmark platform</strong> for those new to federated learning (FL): We built a beginner-friendly FL library and benchmark: <strong>master FL in 2 hours—run it on your PC!</strong>. <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 standard/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>