  Federated learning allows distributed medical institutions to collaboratively
learn a shared prediction model with privacy protection. While at clinical
deployment, the models trained in federated learning can still suffer from
performance drop when applied to completely unseen hospitals outside the
federation. In this paper, we point out and solve a novel problem setting of
federated domain generalization (FedDG), which aims to learn a federated model
from multiple distributed source domains such that it can directly generalize
to unseen target domains. We present a novel approach, named as Episodic
Learning in Continuous Frequency Space (ELCFS), for this problem by enabling
each client to exploit multi-source data distributions under the challenging
constraint of data decentralization. Our approach transmits the distribution
information across clients in a privacy-protecting way through an effective
continuous frequency space interpolation mechanism. With the transferred
multi-source distributions, we further carefully design a boundary-oriented
episodic learning paradigm to expose the local learning to domain distribution
shifts and particularly meet the challenges of model generalization in medical
image segmentation scenario. The effectiveness of our method is demonstrated
with superior performance over state-of-the-arts and in-depth ablation
experiments on two medical image segmentation tasks. The code is available at
"https://github.com/liuquande/FedDG-ELCFS".
