In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

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 …

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 involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while kee** data localized. Training in …

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 …

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

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 …