FedSP: Federated Shared-Personalized Learning for Domain Generalization

Federated Domain Generalization (FedDG) aims to learn models that generalize to unseen domains from distributed client data without accessing clients' raw data. However, existing federated learning methods typically promote collaboration by aggregating client models into a shared global model, implicitly assuming that knowledge beneficial to one client can be effectively shared across all clients. In the presence of domain shift, client-specific visual variations may become entangled with domain-invariant semantic knowledge, making it challenging for the model to determine which knowledge should be shared and which should be personalized. To address this challenge, we propose Federated Shared-Personalized Learning for Domain Generalization (FedSP), which explicitly decouples shared knowledge from client-specific knowledge during federated learning. FedSP employs category prototype-driven cross-domain semantic alignment in the shared module and residual adaptation of domain-inherent discriminative attributes in the personalized module to learn transferable domain-invariant shared representations while modeling domain-specific semantic variations. Additionally, FedSP introduces a multi-scale uncertainty-constrained learning strategy to enhance the model's adaptability across diverse domain shifts. Extensive experiments on multiple domain generalization benchmarks demonstrate that FedSP consistently improves model generalization to unseen domains and outperforms existing federated learning and domain generalization methods. Further analysis shows that explicitly decoupling shared and personalized knowledge facilitates more effective federated collaboration and enhances model robustness under significant domain shifts.