  Traditional federated learning (FL) algorithms operate under the assumption
that the data distributions at training (source domains) and testing (target
domain) are the same. The fact that domain shifts often occur in practice
necessitates equipping FL methods with a domain generalization (DG) capability.
However, existing DG algorithms face fundamental challenges in FL setups due to
the lack of samples/domains in each client's local dataset. In this paper, we
propose StableFDG, a style and attention based learning strategy for
accomplishing federated domain generalization, introducing two key
contributions. The first is style-based learning, which enables each client to
explore novel styles beyond the original source domains in its local dataset,
improving domain diversity based on the proposed style sharing, shifting, and
exploration strategies. Our second contribution is an attention-based feature
highlighter, which captures the similarities between the features of data
samples in the same class, and emphasizes the important/common characteristics
to better learn the domain-invariant characteristics of each class in data-poor
FL scenarios. Experimental results show that StableFDG outperforms existing
baselines on various DG benchmark datasets, demonstrating its efficacy.
