Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server. Clients do not need to submit their local data to the server during training, and hence the local training data of clients is protected. In FL, distributed clients collect their local data independently, so the dataset of each client may naturally form a distinct source domain. In practice, the model trained over multiple source domains may have poor generalization performance on unseen target domains. To address this issue, we propose FedADG to equip federated learning with domain generalization capability. FedADG employs the federated adversarial learning approach to measure and align the distributions among different source domains via matching each distribution to a reference distribution. The reference distribution is adaptively generated (by accommodating all source domains) to minimize the domain shift distance during alignment. In FedADG, the alignment is fine-grained since each class is aligned independently. In this way, the learned feature representation is supposed to be universal, so it can generalize well on the unseen domains. Extensive experiments on various datasets demonstrate that FedADG has better performance than most of the previous solutions even if they have an additional advantage that allows centralized data access. To support study reproducibility, the project codes are available in https://github.com/wzml/FedADG

提出了 FedADG 方法，利用联邦对抗学习来实现域泛化，通过将每个分布与参考分布匹配，逐个类别进行对齐来细粒度地对齐分布，学习到的特征表示可以很好地泛化到未见过的域。

具有领域泛化的联邦学习