In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

With the emergence of data silos and popular privacy awareness, the traditional centralized
approach of training artificial intelligence (AI) models is facing strong challenges. Federated …

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine
learning models with distributed data stored in different silos without exposing sensitive …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

Federated learning (FL) has emerged as a promising technique for preserving user privacy
and ensuring data security in distributed machine learning contexts, particularly in edge …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Abstract The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is
attributable to human activities and behavior. Collecting personal data and executing …