The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

Federated Learning aims to learn machine learning models from multiple decentralized
edge devices (eg mobiles) or servers without sacrificing local data privacy. Recent Natural …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Machine learning is widely used to produce models for a range of applications and is
increasingly offered as a service by major technology companies. However, the required …

Machine learning is widely used in practice to produce predictive models for applications
such as image processing, speech and text recognition. These models are more accurate …

Cross-institutional healthcare predictive modeling can accelerate research and facilitate
quality improvement initiatives, and thus is important for national healthcare delivery …

Machine learning tools have illustrated their potential in many significant sectors such as
healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential …

As 5G and mobile computing are growing rapidly, deep learning services in the Social
Computing and Social Internet of Things (IoT) have enriched our lives over the past few …

Performing machine learning (ML) computation on private data while maintaining data
privacy, aka Privacy-preserving Machine Learning (PPML), is an emergent field of research …

Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …