Multi-Source Collaborative Gradient Discrepancy Minimization for
  Federated Domain Generalization
Yikang Wei Yahong Han
Federated Domain Generalization aims to learn a domain-invariant model from
multiple decentralized source domains for deployment on unseen target domain.
Due to privacy concerns, the data from different source domains are kept
isolated, which poses challenges in bridging the domain gap. To address this
issue, we propose a Multi-source Collaborative Gradient Discrepancy
Minimization (MCGDM) method for federated domain generalization. Specifically,
we propose intra-domain gradient matching between the original images and
augmented images to avoid overfitting the domain-specific information within
isolated domains. Additionally, we propose inter-domain gradient matching with
the collaboration of other domains, which can further reduce the domain shift
across decentralized domains. Combining intra-domain and inter-domain gradient
matching, our method enables the learned model to generalize well on unseen
domains. Furthermore, our method can be extended to the federated domain
adaptation task by fine-tuning the target model on the pseudo-labeled target
domain. The extensive experiments on federated domain generalization and
adaptation indicate that our method outperforms the state-of-the-art methods
significantly.