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 …

Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data
collaboration without revealing their private data to each other. Recently, vertical FL, where …

Leveraging real-world health data for machine learning tasks requires addressing many
practical challenges, such as distributed data silos, privacy concerns with creating a …

Federated learning (FL) has attracted significant interest given its prominent advantages and
applicability in many scenarios. However, it has been demonstrated that sharing updated …

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized
problem-solving technique that allows users to train using massive data. Unprocessed …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance
of remote healthcare systems such as telemedicine. Telemedicine effectively provides …

While rich medical datasets are hosted in hospitals distributed across the world, concerns on
patients' privacy is a barrier against using such data to train deep neural networks (DNNs) …