| Biography | |
|---|---|
 Prof. Kim Phuc Tran University of Lille, Lille, France |
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| Title: Secure, Robust, and Explainable Federated Learning for Smart Healthcare Systems | |
| Abstract: The demand for resources in the healthcare sector, such as clinical doctors, nurses, and medical equipment is steadily increasing which is the aging population around the globe. In order to address such challenges researchers and stakeholders are proposing smart healthcare systems that can assist the healthcare sector while being effective and less costly. In recent decades, deep learning-based smart healthcare systems have outperformed existing approaches in various applications. However, such deep learning models usually require many good quality training data, which brings data privacy concerns among the data owners. In order to the above-mentioned challenges, we proposed a privacy-preserving, efficient, and interpretable/explainable Artificial Intelligence -based end-to-end framework with Federated Learning (FL) to address the limitations of deep learning applications for EEG signal classification and anomaly detection. The key idea of the proposed framework is to train a joint model using updates from local clients who train local models using their local training data, without sharing their raw data directly with other parties. This enhances the privacy of data owners and helps train robust deep-learning models. However, FL alone does not meet the desired criteria for smart healthcare systems. Hence, in order to address such challenges we are developing methods to detect poisoning attacks in FL without ever accessing the local raw data directly, using techniques such as clustering of model updates and majority voting. | |
| Biography: Kim Phuc Tran is currently a Senior Associate Professor (Maître de Conférences HDR, equivalent to a UK Reader) of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France. He received an Engineer's degree and a Master of Engineering degree in Automated Manufacturing. He obtained a Ph.D. in Automation and Applied Informatics at the University of Nantes, and an HDR (Doctor of Science or Dr. habil.) in Computer Science and Automation at the University of Lille, France. His research deals with Explainable Artificial Intelligence, Federated Learning, Reinforcement Learning, Cybersecurity, and Decision Support Systems with applications in industrial systems, such as production systems, industrial control systems, and healthcare systems. He is the Associate Editor, Editorial Board Member, and Guest Editor for several international journals such as IEEE Transactions on Intelligent Transportation Systems and Engineering Applications of Artificial Intelligence. He has been or is involved (co-PI or member) in several national and European projects. He is an expert and evaluator for the Research and Innovation program of the Government of the French Community, Belgium. He received the Award for Scientific Excellence (Prime d’Encadrement Doctoral et de Recherche) given by the Ministry of Higher Education, Research and Innovation, France for 4 years from 2021 to 2025 in recognition of his outstanding scientific achievements. From 2017 until now, he has been the Senior Scientific Advisor at Dong A University and the International Research Institute for Artificial Intelligence and Data Science (IAD), Danang, Vietnam where he has held the International Chair in Data Science and Explainable Artificial Intelligence. | |
