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.

我们提出了一种多源协作梯度差异最小化（MCGDM）方法来实现联邦领域泛化，通过在原始图像和增强图像之间进行域内梯度匹配来避免过拟合孤立领域内的领域特定信息，同时通过与其他领域的合作进行域间梯度匹配，进一步减小分散领域之间的领域差异，通过结合域内和域间梯度匹配，我们的方法在未知领域上具有良好的泛化能力，同时还可以通过在伪标记的目标领域上微调目标模型来扩展到联邦领域适应任务，在联邦领域泛化和适应的广泛实验表明，我们的方法明显优于现有的方法。

多源协同梯度差异最小化的联邦域泛化