Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

Radiomics is a quantitative approach to analyzing medical multi-layered images in
combination with molecular, genetic and clinical information, which has evidenced very …

In federated learning (FL), model performance typically suffers from client drift induced by
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …

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

In federated learning, a strong global model is collaboratively learned by aggregating
clients' locally trained models. Although this precludes the need to access clients' data …

Abstract Personalized Federated Learning (pFL), which utilizes and deploys distinct local
models, has gained increasing attention in recent years due to its success in handling the …

Abstract Model fusion without accessing training data in machine learning has attracted
increasing interest due to the practical resource-saving and data privacy issues. During the …

Federated learning (FL) is an emerging machine learning paradigm that allows multiple
parties to train a shared model collaboratively in a privacy-preserving manner. Existing …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …