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.

翻译：传统联邦学习算法假设训练数据（源域）与测试数据（目标域）的分布一致。然而实际应用中常出现域偏移现象，亟需为联邦学习赋予域泛化能力。现有域泛化算法在联邦学习场景下面临根本性挑战：各客户端的本地数据集缺乏足够的样本/域。本文提出StableFDG——一种基于风格与注意力学习的联邦域泛化策略，主要包含两项关键贡献。第一是基于风格的学习，使每个客户端能在本地数据集基础上探索超越原始源域的新颖风格，通过所提出的风格共享、迁移与探索策略提升域多样性。第二是基于注意力的特征高亮模块，该模块能捕捉同类数据样本特征间的相似性，突出重要/共同特征，从而在数据匮乏的联邦学习场景中更好地学习每个类别的域不变特性。实验结果表明，StableFDG在多个域泛化基准数据集上优于现有基线方法，验证了其有效性。