Biography

Ziqi Wang is a Ph.D. candidate in Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he works at the Chair for Dynamics, Control, Machine Learning and Numerics–Alexander von Humboldt Professorship, under the supervision of Prof. Dr. Enrique Zuazua.

His research focuses on federated learning, distributed optimization, and dynamical systems. He is particularly interested in federated learning from a multi-level perspective: 1) server-side fair and robust aggregation; 2) client-side drift control and local optimization; 3) system-level game-theoretic incentives; and 4) privacy risks in distributed training.

During his visit with Prof. Dr. Shadi Albarqouni’s group, he is exploring how conflict-resolved, fairness-aware aggregation algorithms for federated learning can support privacy-preserving collaborative learning in healthcare.