Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Neural collapse (NC) refers to the surprising structure of the last layer of deep neural
networks in the terminal phase of gradient descent training. Recently, an increasing amount …

Classical federated learning (FL) enables training machine learning models without sharing
data for privacy preservation, but heterogeneous data characteristic degrades the …

Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability
to support heterogeneous models and data. To reduce the high communication cost of …

Prevalent federated learning commonly develops under the assumption that the ideal global
class distributions are balanced. In contrast, real-world data typically follows the long-tailed …

Abstract Heterogeneous Federated Learning (HtFL) enables collaborative learning on
multiple clients with different model architectures while preserving privacy. Despite recent …

Federated learning (FL) is a promising approach for healthcare institutions to train high-
quality medical models collaboratively while protecting sensitive data privacy. However, FL …

In this paper, we focus on a general yet important learning problem, pairwise similarity
learning (PSL). PSL subsumes a wide range of important applications, such as open-set …

Abstract Personalized Federated Learning (PFL) is primarily designed to provide
customized models for each client to better fit the non-iid distributed client data which is a …