Dynamic Representation Modeling for Federated Medical Image Domain Generalization

Federated Domain Generalization (FDG) aims to learn a global model robust to heterogeneous domain shifts without sharing raw data, a critical challenge in multi-center medical imaging. Most existing methods implicitly assume static inter-domain discrepancies, overlooking the fact that client representations evolve continuously during local optimization. We identify this phenomenon as representation dynamics, which refers to the temporal evolution of latent features induced by training updates rather than shifts in underlying data distributions. Such dynamics often lead to unstable aggregation, particularly in heterogeneous medical imaging scenarios. To address this issue, we propose Dynamic Knowledge Tracked FDG (DKT-FDG), a framework that explicitly tracks these dynamics by leveraging flow matching to model continuous representation trajectories. By aligning representation evolution instead of static snapshots, DKT-FDG improves robustness to domain shifts. Comprehensive experiments on multiple multi-center medical benchmarks demonstrate consistent improvements over state-of-the-art FDG methods, highlighting the importance of modeling representation dynamics in federated learning.