add a new FedCross algorithm

Author: TsingZ0

docs/algo.html

@@ -216,7 +216,7 @@ <h1><img src="imgs/logo-green.png" alt="icon" height="36" style="vertical-align:
         <div class="content">
             <section id="algorithm">
                 <h2>Algorithms With Code (Updating)</h2>
-                <p>We categorize FL algorithms into 8 tFL algorithms and 29 pFL algorithms based on their foundational techniques. The detailed classification is outlined below.</p>
+                <p>We categorize FL algorithms into tFL and pFL algorithms based on their foundational techniques. The detailed classification is outlined below.</p>
                 <h3>Traditional FL (tFL)</h3>
 
                 <ul>
@@ -248,6 +248,12 @@ <h3>Traditional FL (tFL)</h3>
                 </ul>
                 </ul>
 
+                <li><strong><em>Heuristically-search-based tFL</em></strong></li>
+                <ul>
+                    <li><strong>FedCross</strong> — <a href="https://www.computer.org/csdl/proceedings-article/icde/2024/171500c137/1YOuaPcHF3q">FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation</a> <em>ICDE 2024</em></li>
+                </ul>
+                </ul>
+
                 <h3>Personalized FL (pFL)</h3>
 
                 <ul>

docs/docs.html

@@ -242,7 +242,7 @@ <h4>Simple File Structure</h4>
 
                 <h4>Key Features</h4>
                 <p>
-                    <li><strong>38</strong> traditional FL (tFL) or personalized FL (pFL) algorithms,  <strong>3</strong> scenarios, and <strong>24</strong> datasets.</li>
+                    <li><strong>39</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 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="features.html#privacy-evaluation">privacy evaluation</a> and <a href="features.html#systematical-research-supprot">systematical research support</a>.</li>

docs/index.html

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     <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> and <strong>benchmark</strong>. <br /> <strong>38</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</strong>. <br /> <strong>39</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>