Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Federated learning (FL) is a promising framework for privacy-preserving and distributed
training with decentralized clients. However, there exists a large divergence between the …

Abstract Federated Domain Generalization aims to learn a domain-invariant model from
multiple decentralized source domains for deployment on unseen target domain. Due to …

Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A promising
approach to improve model generalization in DG is the identification of flat minima. One …

Conventional unsupervised domain adaptation (UDA) and domain generalization (DG)
methods rely on the assumption that all source domains can be directly accessed and …

Contemporary domain generalization methods have demonstrated effectiveness in aiding
the generalized diagnosis of medical images with multi-source data by joint optimization …

Federated learning is a distributed paradigm that allows multiple parties to collaboratively
train deep learning models without direct exchange of raw data. Nevertheless, the inherent …

In some real-world applications, data samples are usually distributed on local devices,
where federated learning (FL) techniques are proposed to coordinate decentralized clients …

Domain generalization (DG) aims to learn from multiple source domains a model that can
generalize well on unseen target domains. Existing DG methods mainly learn the …