StableFDG: Style and Attention Based Learning for Federated Domain Generalization
Traditional federated learning (FL) algorithms operate under the assumption that the data distributions at training (source domains) and testing (target domain) are the same. The fact that domain shifts often occur in practice necessitates equipping FL methods with a domain generalization (DG) capability. However, existing DG algorithms face fundamental challenges in FL setups due to the lack of samples/domains in each client's local dataset. In this paper, we propose StableFDG, a style and attention based learning strategy for accomplishing federated domain generalization, introducing two key contributions. The first is style-based learning, which enables each client to explore novel styles beyond the original source domains in its local dataset, improving domain diversity based on the proposed style sharing, shifting, and exploration strategies. Our second contribution is an attention-based feature highlighter, which captures the similarities between the features of data samples in the same class, and emphasizes the important/common characteristics to better learn the domain-invariant characteristics of each class in data-poor FL scenarios. Experimental results show that StableFDG outperforms existing baselines on various DG benchmark datasets, demonstrating its efficacy.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityDomain GeneralizationFederated LearningSimilar Papers 제목 키워드 기반
Multi-Source Collaborative Style Augmentation and Domain-Invariant Learning for Federated Domain Generalization
Federated domain generalization aims to learn a generalizable model from multiple decentralized source domains for deploying on the unseen target domain. The style augmentation methods have achieved great progress on dom…
Domain GeneralizationMLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a cl…
Domain GeneralizationFederated LearningImage SegmentationMedical Image Segmentation+1Federated style aware transformer aggregation of representations
Personalized Federated Learning (PFL) faces persistent challenges, including domain heterogeneity from diverse client data, data imbalance due to skewed participation, and strict communication constraints. Traditional fe…
Personalized Federated LearningFedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization
Federated Domain Generalization (FedDG) aims to train the global model for generalization ability to unseen domains with multi-domain training samples. However, clients in federated learning networks are often confined t…
Domain GeneralizationFederated LearningDecentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis
Much of the federated learning (FL) literature focuses on settings where local dataset statistics remain the same between training and testing time. Recent advances in domain generalization (DG) aim to use data from sour…
Domain GeneralizationFederated Learning