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 …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

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

Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …

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 …

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 …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

This paper proposes a novel federated recommendation framework that incorporates
differential privacy to safeguard user privacy without compromising on the accuracy of …