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 	<p><b><font  size="4">Selected Publications</font></b></p>
 	<p style="margin-left: 24px; width: 620px;"><font  style="font-size: 11pt;">For the most recent works, the pre-print can be found on <a href="http://arxiv.org/a/deng_j_1" target="_blank">arXiv</a>, the code are hosted on <a href="https://github.com/Rand2AI" target="_blank">GitHub</a>.</font></p>
+	<p style="margin-left: 36px; width: 620px;"><font style="font-size: 10pt;"><b>Federated Deep Learning</b></font></p>
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+		<li><p align="justify"><font  size="2"><img height="15px" border="0" src="./img/newz.gif" style="margin-left: 0px; margin-right:7px;">📑 Ren H., Deng J., Xie X., Ma X. and Wang Y., <i>FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition</i>, Neurocomputing, 2023. [ <a href="https://doi.org/10.1016/j.neucom.2023.127126" target="_blank">Elsevier</a> | <a href="https://arxiv.org/abs/2007.07296" target="_blank">Pre-Print</a> | <a href="https://github.com/Rand2AI/FedBoosting" target="_blank">Code-PyTorch</a> ]</font></p></li>
 		<li><p align="justify"><font  size="2"><img height="15px" border="0" src="./img/newz.gif" style="margin-left: 0px; margin-right:7px;">📑 Xie X., Hu C., Ren H. and Deng J. <i>A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective</i>, arXiv:2311.16065, 2023. [ <a href="https://arxiv.org/abs/2311.16065" target="_blank">Pre-Print</a> | <a href="https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning" target="_blank">Github-Page</a> ]</font></p></li>
 		<li><p align="justify"><font  size="2">📑 Ren H., Deng J., Xie X., Ma X. and Ma J., <i>Gradient Leakage Defense with Key-Lock Module for Federated Learning</i>, arXiv:2305.04095, 2023. [ <a href="https://arxiv.org/abs/2305.04095" target="_blank">Pre-Print</a> | <a href="https://github.com/Rand2AI/FedKL" target="_blank">Code-PyTorch</a> ]</font></p></li>
 		<li><p align="justify"><font  size="2">📑 Ren H., Deng J. and Xie X., <i>GRNN: Generative Regression Neural Network - A Data Leakage Attack for Federated Learning</i>, ACM Transactions on Intelligent Systems and Technology (TIST), 2022. [ <a href="https://dl.acm.org/doi/10.1145/3510032" target="_blank">ACM</a> | <a href="https://arxiv.org/abs/2105.00529" target="_blank">Pre-Print</a> | <a href="https://github.com/Rand2AI/GRNN" target="_blank">Code-PyTorch</a> | <a href="https://worldwide.espacenet.com/patent/search/family/076377782/publication/WO2022223629A1?q=pn%3DWO2022223629A1" target="_blank">Patent</a> (WO2022223629A1, Pending) ]</font></p></li>
+	</ul>
+	<p style="margin-left: 36px; width: 620px;"><font style="font-size: 10pt;"><b>Image Detection & Segmentation</b></font></p>
+	<ul style="width: 600px;">
 		<li><p align="justify"><font  size="2">📑 Deng J. and Xie X., <i>3D Interactive Segmentation with Semi-Implicit Representation and Active Learning</i>, IEEE Transactions on Image Processing (TIP), 2021. [ <a href="https://ieeexplore.ieee.org/document/9611087" target="_blank">IEEE</a> | <a href="./src/NU-IBS.Code.zip" target="_blank">Code-Xcode</a> ]</font></p></li> 
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