Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks

Federated Domain Generalization (FDG) aims to train a global model that generalizes well to new clients in a privacy-conscious manner, even when domain shifts are encountered. The increasing concerns of knowledge generalization and data privacy also challenge the traditional gather-and-analyze paradigm in networks. Recent investigations mainly focus on aggregation optimization and domain-invariant representations. However, without directly considering the data augmentation and leveraging the knowledge among existing domains, the domain-only data cannot guarantee the generalization ability of the FDG model when testing on the unseen domain. To overcome the problem, this paper proposes a distributed data augmentation method which combines Generative Adversarial Networks (GANs) and Federated Analytics (FA) to enhance the generalization ability of the trained FDG model, called FA-FDG. First, FA-FDG integrates GAN data generators from each Federated Learning (FL) client. Second, an evaluation index called generalization ability of domain (GAD) is proposed in the FA server. Then, the targeted data augmentation is implemented in each FL client with the GAD index and the integrated data generators. Extensive experiments on several data sets have shown the effectiveness of FA-FDG. Specifically, the accuracy of the FDG model improves up to 5.12% in classification problems, and the R-squared index of the FDG model advances up to 0.22 in the regression problem.