G2G: Generalized Learning by Cross-Domain Knowledge Transfer for Federated Domain Generalization

Abstract

We propose G2G, based on the global model of Generalized learning to solve the Federated Domain Generalization (FedDG) task. FedDG aims to collaboratively train a global model that can directly generalize to the unseen target domain without data sharing. Existing methods face challenges from both data heterogeneity, arising from imbalanced as well as non-independent and identical distributions (non-IID) among all domains, and model heterogeneity due to personalized requirements for client models. Also, these methods suffer from unnecessary time cost due to aggregation and distribution. G2G addresses these issues by making the global model acquire in-domain classification knowledge during local knowledge transfer and gain extensive knowledge in cross-domain training. Moreover, G2G eliminates waiting time by allowing clients to train independently when not trained with the global model. G2G outperforms state-of-the-art (SOTA) methods by 1.52%, 2.35%, and 1.11% on three datasets, respectively.