Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative
machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks …

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 …

Various collaborative distributed machine learning (CDML) systems, including federated
learning systems and swarm learning systems, with different key traits were developed to …

Anonymization methods have gained widespread use in safeguarding privacy. However,
conventional anonymization solutions inevitably lead to the loss of semantic information …

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 …

Personalized federated learning (PFL) addresses the significant challenge of non-
independent and identically distributed (non-IID) data across clients in federated learning …

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

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 …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
collaborative training. Under domain skew the current FL approaches are biased and face …

Privacy preservation is a critical concern in Federated Learning (FL). However, traditional
Local Differential Privacy (LDP) methods face challenges in balancing FL model accuracy …