Qiaolun Zhang

Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization

Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG methods typically struggle with cross-client data heterogeneity and incur significant communication and computation overhead