þ Master Supervisor

Research Areas

Privacy Computing and Federated Learning

Security and Privacy of Deep Network Models: Gradient Inversion Attacks and Defense Methods, Model Inversion Attacks and Defense Methods

Security and Privacy in the Internet of Things

Publications

l Wu X, Chen Y*, Yu H, Yang Z. Privacy-preserving federated learning based on noise addition[J]. Expert Systems with Applications. Volume 267, 2025, 126228. (JCR-Q1 TOP, IF= 7.5, First author is a co-supervised PhD student).

l Chen Y, Yang S, José-Fernán Martínez, Lourdes López, Yang Z*, A resilient group-based multi-subset data aggregation scheme for smart grid, IEEE Internet of Things Journal, 2023: 1–1. (JCR-Q1 TOP, IF= 10.238).

l Yang S, Chen Y, Tu S, Yang Z*, Li B, Liu H. Fast Secure Aggregation with High Dropout Resilience for Federated Learning [J]// IEEE Transactions on Green Communications and Networking., 2023: 1–1. （JCR-Q2，IF= 3.525，First author is a co-supervised PhD student）

l AAIA:An Efficient Aggregation Scheme Against Inverting Attack for Federated Learning,Yang Z, Yang S, José-Fernán Martínez, Lourdes López, Chen Y*, International Journal of Information Security, (JCR-Q2, IF= 2.427).

l Chen Y, Martínez J-F, López L, Yu H, Yang Z. A dynamic membership group-based multiple-data aggregation scheme for smart grid[J]. IEEE Internet of Things Journal, 2021: 1–1. (JCR-Q1 TOP, IF= 10.238)

Personal Statement

Chen Yuwen, Ph.D., is a Master's Supervisor affiliated with the School of Computer Science, Faculty of Information Technology, Beijing University of Technology. His primary research focuses on artificial intelligence security and privacy computing. He has published over 10 papers in internationally renowned journals and conferences such as IEEE TGCN and IoTJ. He encourages students to aim at solving significant scientific and practical problems, emphasizing the importance of attitude and perseverance. He believes that even students from top-tier universities may face limited development if they retreat at the first sight of challenges. He motivates students to engage in research during their junior and senior years to familiarize themselves with relevant background knowledge. Most students in his research group complete one academic paper per year. He currently teaches the following courses:

Security of Deep Networks and AI Technologies (Spring Semester, Undergraduate, 2023–Present)

Security of Artificial Intelligence (Spring Semester, Graduate, 2023–Present)

Intelligent Information Processing (Fall Semester, Undergraduate, 2024–Present)