Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

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 …

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 …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

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 …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

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 …

With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …