This paper focuses on addressing the practical yet challenging problem of model
heterogeneity in federated learning, where clients possess models with different network …

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …

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 …

Medical Image Analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

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

Federated learning facilitates the collaborative learning of a global model across multiple
distributed medical institutions without centralizing data. Nevertheless the expensive cost of …

Federated learning enables multiple hospitals to cooperatively learn a shared model without
privacy disclosure. Existing methods often take a common assumption that the data from …

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

Federated Learning (FL) is an effective framework for a distributed system that constructs a
powerful global deep learning model, which diminishes the local bias and accommodates …

Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed
way, allowing for privacy protection. However, the real-world non-IID data will lead to client …