Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

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

One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

This work considers the category distribution heterogeneity in federated learning. This issue
is due to biased labeling preferences at multiple clients and is a typical setting of data …

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

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

Federated learning (FL) can be used to improve data privacy and efficiency in magnetic
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
learn a powerful global model by aggregating local training. However, the performance of …