rename FD

Author: TsingZ0

README.md

@@ -112,7 +112,7 @@ The origin of the **data heterogeneity** phenomenon is the characteristics of us
 
   ***Knowledge-distillation-based pFL (more in [HtFLlib](https://github.com/TsingZ0/HtFLlib))***
 
-- **FedDistill (FD)** — [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://arxiv.org/pdf/1811.11479.pdf) *2018*
+- **FD (FedDistill)** — [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://arxiv.org/pdf/1811.11479.pdf) *2018*
 - **FML** — [Federated Mutual Learning](https://arxiv.org/abs/2006.16765) *2020*
 - **FedKD** — [Communication-efficient federated learning via knowledge distillation](https://www.nature.com/articles/s41467-022-29763-x) *Nature Communications 2022*
 - **FedProto** — [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://ojs.aaai.org/index.php/AAAI/article/view/20819) *AAAI 2022*

docs/algo.html

@@ -290,7 +290,7 @@ <h3>Personalized FL (pFL)</h3>
 
                 <li><strong><em>Knowledge-distillation-based pFL (more in <a href="https://github.com/TsingZ0/HtFLlib">HtFLlib</a>)</em></strong></li>
                 <ul>
-                    <li><strong>FedDistill (FD)</strong> — <a href="https://arxiv.org/pdf/1811.11479.pdf">Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data</a> <em>2018</em></li>
+                    <li><strong>FD (FedDistill)</strong> — <a href="https://arxiv.org/pdf/1811.11479.pdf">Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data</a> <em>2018</em></li>
                     <li><strong>FML</strong> — <a href="https://arxiv.org/abs/2006.16765">Federated Mutual Learning</a> <em>2020</em></li>
                     <li><strong>FedKD</strong> — <a href="https://www.nature.com/articles/s41467-022-29763-x">Communication-efficient federated learning via knowledge distillation</a> <em>Nature Communications 2022</em></li>
                     <li><strong>FedProto</strong> — <a href="https://ojs.aaai.org/index.php/AAAI/article/view/20819">FedProto: Federated Prototype Learning across Heterogeneous Clients</a> <em>AAAI 2022</em></li>