Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

Federated learning enables multiple parties to collaboratively train a machine learning
model without communicating their local data. A key challenge in federated learning is to …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

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 …

Collaborative and federated learning has become an emerging solution to many industrial
applications where data values from different sites are exploit jointly with privacy protection …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …