Projects

Personalized Federated Learning

In the standard Federated Learning (FL) paradigm, multiple clients aim to learn models cooperatively without directly sharing their private data. However, clients may have different data distributions and training objectives that cannot be customized in FL. As a result, the performance of the learned model is downgraded, and FL learning cannot fully mine the data of clients, especially clients with non-iid data. My research explores the possibility of personalized FL architecture and FL aggregation algorithms to achieve personalized learning objectives for each client and minimize/remove the performance sacrifice of any other client. The research progress and outcomes are distributed in the paper collection.

• Y. Hu, Y. Huang, Z. Xie, J. Pang, C. Cheng, Z. Cai
  FedDiDy: Federated Class-Incremental Fault Diagnosis for Industrial IoT Rotating Machinery Under Dynamic Edge Participation
  IEEE Internet of Things Journal, 2026. (IF: 8.9)
• Y. Hu, Y. Huang, Z. Xie, J. Pang, C. Cheng
  Physics-Augmented Federated Continual Learning for Rotating Machinery Fault Diagnosis
  Neurocomputing, 2026. (IF: 6.7)
• Z. Cai, J. Pang, Y. Li, Y. Huang, Z. Xie
  A Comprehensive Survey of Federated Open-World Learning
  IEEE Transactions on Network Science and Engineering, 2025. (IF: 7.9)
• G. Yuan, J. Li, Y. Huang, Z. Xie, J. Pang, Z. Cai
  Independence and Unity: Unseen Domain Segmentation Based on Federated Learning
  IEEE Internet of Things Journal, 2023. (IF: 10.6)
• C. Jing, Y. Huang, Y. Zhuang, L. Sun, Y. Huang, Z. Xiao, X. Ding
  Exploring Personalization via Federated Representation Learning on Non-IID Data
  Neural Networks, 2023. (IF: 9.657)
• Z. Xie, Y. Huang, D. Yu, R.M. Parizi, Y. Zheng, J. Pang
  FedEE: A Federated Graph Learning Solution for Extended Enterprise Collaboration
  IEEE Transactions on Industrial Informatics, 2022. (IF: 11.648)
• J. Pang, Y. Huang, Z. Xie, J. Li, Z. Cai
  Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution
  Tsinghua science and technology, 2021.
  (Excellent Paper Award）(Hot Paper by Web of Science)
• J. Pang, Y. Huang*, Z. Xie, Q. Han, Z. Cai
  Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT IEEE Internet of Things Journal, 2020. (IF: 10.6)

Trustworthy and Secure Embodied AI

This project studies the security and behavioral reliability of embodied AI and vision–language–action (VLA) models. The research focuses on developing trustworthy evaluation and adaptation methods for intelligent systems operating in heterogeneous environments.

VR Cybersecurity Education for Middle School Students

This is an NSA/NSF GenCyber program supported project. We build six modules: Digital Footprints, Trojan Horse/Ransomware, Cryptography, Hacking Ethics, Authentication & Authorization, and Phishing to augment cybersecurity education for middle school students using VR lectures and games. A video of this project will be posted when it is fully completed in Summer 2024.

To see the details or request a free version of the project, please see https://github.com/research-web/Cyber-Security-Park