FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

By Quande Liu, Cheng Chen, Jing Qin, Qi-Yan Dou, P. Heng. Computer Vision and Pattern Recognition, 2021

617 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d6a6d8f56c5db24bfae7c614f21195dc0e89f4a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Tao Qin
3. Bo Du
4. Mang Ye
5. Li Shen
6. Wenke Huang
7. Cuiling Lan
8. Carl Yang

Citations in the corpus

• Federated learning for medical image analysis: A survey (2023, methodology): “Currently, there are a few works that introduce domain generalization into federated learning [39], [133].”
• Federated learning and differential privacy for medical image analysis (2021, background): “FedDG proposed by Liu et al. 14 is a privacy-preserving solution to learn a generalizable FL model through an effective continuous frequency space interpolation mechanism across clients.”
• Generalizing to Unseen Domains: A Survey on Domain Generalization (2021, methodology): “2 [216, 100, 159, 52, 135, 58, 40, 216, 81, 59] Semi-supervised domain generalization Strain is partially labeled [170, 217] Federated domain generalization Strain cannot broadcast to the server [218, 219, 138] Open domain generalization Ytrain 6= Ytest [54] Unsupervised domain generalization Strain”
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning (2022, background): “There exist extensive works on improving the global performance of FL via client selection [4, 7, 15], split learning [11, 38], domain adaptation [26, 33], etc.”
• Learn from Others and Be Yourself in Heterogeneous Federated Learning (2022, background): “Latest researches have studied related problems of unsupervised domain adaptation for target domain [45, 65] and domain generalization on unseen domains [52].”
• Subgraph Federated Learning with Missing Neighbor Generation (2021, background): “…and natural language processing (NLP) to allow the joint training of powerful and generalizable deep convolutional neural networks and language models on separately stored datasets of images and texts (Liu et al., 2021; Dou et al., 2021; Liang et al., 2019; Zhu et al., 2020b; He et al., 2021b).”