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 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 …

The microservices architecture is extensively utilized in cloud-based application
development, characterized by the construction of applications through a series of …

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 …

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 recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Federated learning aims to facilitate collaborative training among multiple clients with data
heterogeneity in a privacy-preserving manner, which either generates the generalized …

Federated learning (FL) enables multiple data sources to collaboratively train a global
model for Multi-source Visual Fusion and Understanding (MSVFU) without centralizing raw …

Digital Twins (DTs) support real time analysis and provide a reliable simulation platform for
the Internet of Vehicles (IoV). DT modeling relies on a large amount of data, based on their …