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 …

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 …

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

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 …

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

Abstract Personalized Federated Graph Learning (pFGL) facilitates the decentralized
training of Graph Neural Networks (GNNs) without compromising privacy while …

Federated learning (FL) enables edge devices to cooperatively train models without
exposing their raw data. However, implementing a practical FL system at the network edge …

Federated Learning (FL) enables collaborative training of Machine Learning (ML) models
across decentralized clients while preserving data privacy. One of the challenges that FL …

Federated Learning (FL) stands as a privacy-preserving machine learning paradigm that
enables collaborative training of a global model across multiple clients. However, the …