fix typos

README.md

@@ -1,6 +1,6 @@
 # PFLlib: Personalized Federated Learning Library
 
-🎯***We create a beginner-friendly algorithm library and evaluation platform for those new to federated learning. Join us in expanding the FL community by contributing your algorithms, datasets, and metrics to this project.***
+🎯***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 now has its official website and domain name: https://www.pfllib.com/!!!**
 
@@ -33,13 +33,13 @@ Figure 1: An Example for FedAvg. You can create a scenario using `generate_DATA.
 
 - Refer to [this guide](#how-to-start-simulating-examples-for-fedavg) to learn how to use it.
 
-- The evaluation platform can simulate scenarios using the 4-layer CNN on Cifar100 for **500 clients** on **one NVIDIA GeForce RTX 3090 GPU card** with only **5.08GB GPU memory** cost.
+- The benchmark platform can simulate scenarios using the 4-layer CNN on Cifar100 for **500 clients** on **one NVIDIA GeForce RTX 3090 GPU card** with only **5.08GB GPU memory** cost.
 
 - We provide [privacy evaluation](#privacy-evaluation) and [systematical research supprot](#systematical-research-supprot). 
 
 - You can now train on some clients and evaluate performance on new clients by setting `args.num_new_clients` in `./system/main.py`. Please note that not all tFL/pFL algorithms support this feature.
 
-- PFLlib primarily focuses on data (statistical) heterogeneity. For algorithms and an evaluation platform that address **both data and model heterogeneity**, please refer to our extended project **[Heterogeneous Federated Learning (HtFLlib)](https://github.com/TsingZ0/HtFLlib)**.
+- PFLlib primarily focuses on data (statistical) heterogeneity. For algorithms and a benchmark platform that address **both data and model heterogeneity**, please refer to our extended project **[Heterogeneous Federated Learning (HtFLlib)](https://github.com/TsingZ0/HtFLlib)**.
 
 - As we strive to meet diverse user demands, frequent updates to the project may alter default settings and scenario creation codes, affecting experimental results.
 
@@ -381,7 +381,7 @@ This library is designed to be easily extendable with new algorithms and dataset
   
 - **New Optimizer**: If you need a new optimizer for training, add it to `./system/flcore/optimizers/fedoptimizer.py`.
   
-- **New Evaluation Platform or Library**: The evaluation framework is flexible, allowing users to build custom platforms or libraries for specific applications, such as [FL-IoT](https://github.com/TsingZ0/FL-IoT) and [HtFLlib](https://github.com/TsingZ0/HtFLlib).
+- **New Benchmark Platform or Library**: Our framework is flexible, allowing users to build custom platforms or libraries for specific applications, such as [FL-IoT](https://github.com/TsingZ0/FL-IoT) and [HtFLlib](https://github.com/TsingZ0/HtFLlib).
 
 
 ## Privacy Evaluation

docs/about.html

@@ -116,7 +116,7 @@ <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 an evaluation 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). <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>

docs/benchmark.html

@@ -181,8 +181,8 @@ <h1><a href="index.html">PFLlib</a></h1>
         </div>
         <div class="content">
             <section id="intro">
-                <h2>Benchmark & Evaluation Platform</h2>
-                <p>To integrate all the algorithms, datasets, and scenarios, we standardize the experimental settings and create a <strong>unified evaluation platform</strong> for a fair comparison of these algorithms. Here, we present the benchmark results of <strong>20 algorithms</strong> across two widely-used <em><strong>label skew</strong></em> scenarios. This is just one example. You can obtain different results by resetting all the configurations in <code>main.py</code> in our PFLlib.</p>
+                <h2>Benchmark Platform</h2>
+                <p>To integrate all the algorithms, datasets, and scenarios, we standardize the experimental settings and create a <strong>unified benchmark platform</strong> for a fair comparison of these algorithms. Here, we present the benchmark results of <strong>20 algorithms</strong> across two widely-used <em><strong>label skew</strong></em> scenarios. This is just one example. You can obtain different results by resetting all the configurations in <code>main.py</code> in our PFLlib.</p>
                 <h3>Leaderboard</h3>
                 <p>Our methods—<a href="https://github.com/TsingZ0/FedCP"><strong>FedCP</strong></a>, <a href="https://github.com/TsingZ0/GPFL"><strong>GPFL</strong></a>, and <a href="https://github.com/TsingZ0/DBE"><strong>FedDBE</strong></a>—lead the way. Notably, <strong>FedDBE</strong> stands out with robust performance across varying data heterogeneity levels.</p>
                 <h4 style="width:100%; text-align:center;">The test accuracy (%) on the CV and NLP tasks in <em>label skew</em> settings.</h4>

docs/docs.html

@@ -201,10 +201,10 @@ <h4>Key Features</h4>
                 <p>
                     <li><strong>37</strong> traditional FL (tFL) or personalized FL (pFL) algorithms,  <strong>3</strong> scenarios, and <strong>24</strong> datasets.</li>
                     <li>Some experimental results are avalible in the <a href="https://arxiv.org/abs/2312.04992"><strong>PFLlib paper</strong></a> and <a href="benchmark.html"><strong>Benchmark Results</strong></a>.</li>
-                    <li>The evaluation platform can simulate scenarios using the 4-layer CNN on Cifar100 for <strong>500 clients</strong> on one NVIDIA GeForce RTX 3090 GPU card with <strong>only 5.08GB GPU memory cost</strong>.</li>
+                    <li>The benchmark platform can simulate scenarios using the 4-layer CNN on Cifar100 for <strong>500 clients</strong> on one NVIDIA GeForce RTX 3090 GPU card with <strong>only 5.08GB GPU memory cost</strong>.</li>
                     <li>We provide <a href="#privacy-evaluation">privacy evaluation</a> and <a href="#systematical-research-supprot">systematical research support</a>.</li>
                     <li>You can now train on some clients and evaluate performance on new clients by setting <code>args.num_new_clients</code> in <code>./system/main.py</code>. Please note that not all tFL/pFL algorithms support this feature.</li>
-                    <li>PFLlib primarily focuses on data (statistical) heterogeneity. For algorithms and an evaluation platform that address <strong>both data and model heterogeneity</strong>, please refer to our extended project <a href="https://github.com/TsingZ0/HtFLlib"><strong>HtFLlib</strong></a>.</li>
+                    <li>PFLlib primarily focuses on data (statistical) heterogeneity. For algorithms and a benchmark platform that address <strong>both data and model heterogeneity</strong>, please refer to our extended project <a href="https://github.com/TsingZ0/HtFLlib"><strong>HtFLlib</strong></a>.</li>
                     <li>As we strive to meet diverse user demands, frequent updates to the project may alter default settings and scenario creation codes, affecting experimental results.</li>
                     <li><a href="https://github.com/TsingZ0/PFLlib/issues?q=is%3Aissue+is%3Aclosed"><strong>Closed issues</strong></a> may help you a lot when errors arise.</li>
                 </p>

docs/extend.html

@@ -250,7 +250,7 @@ <h2>Easy to Extend</h2>
                   </li>
 
                   <li>
-                    <strong>New Evaluation Platform or Library</strong>: The evaluation framework is flexible, allowing users to build 
+                    <strong>New Benchmark Platform or Library</strong>: Our code framework is flexible, allowing users to build 
                     custom platforms or libraries for specific applications, such as 
                     <a href="https://github.com/TsingZ0/FL-IoT">FL-IoT</a> and 
                     <a href="https://github.com/TsingZ0/HtFLlib">HtFLlib</a>.

docs/index.html

@@ -194,7 +194,7 @@ <h1><a href="index.html" id="PFLlib">PFLlib</a></h1>
     <div class="hero">
         <div>
             <h1>PFLlib is all you need</h1>
-            <p>A  <strong>beginner-friendly</strong> and comprehensive personalized federated learning <strong>library</strong>, <strong>benchmark</strong>, and <strong>evaluation platform</strong>. <br /> <strong>37</strong> traditional FL (tFL) or personalized FL (pFL) algorithms,  <strong>3</strong> scenarios, and <strong>24</strong> datasets.</p>
+            <p>A <strong>beginner-friendly</strong> and comprehensive personalized federated learning <strong>library</strong> and <strong>benchmark platform</strong>. <br /> <strong>37</strong> traditional FL (tFL) or personalized FL (pFL) algorithms,  <strong>3</strong> scenarios, and <strong>24</strong> datasets.</p>
             <button onclick="window.location.href='docs.html'">Get Started</button>
         </div>
     </div>