Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …

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

Abstract Model heterogeneous federated learning is a challenging task since each client
independently designs its own model. Due to the annotation difficulty and free-riding …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

Abstract Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple
hospitals to collaborate distributedly without aggregating local data, thereby protecting …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …

Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …