Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

Federated learning (FL) rests on the notion of training a global model in a decentralized
manner. Under this setting, mobile devices perform computations on their local data before …

Federated learning utilizes various resources provided by participants to collaboratively train
a global model, which potentially address the data privacy issue of machine learning. In …

Federated Learning (FL) is a distributed learning framework that can deal with the
distributed issue in machine learning and still guarantee high learning performance …

The sixth-generation (6G) wireless network is conceptualized to provide ubiquitous and
reliable network access through effective inter-networking among space, air, and terrestrial …

Federated learning (FL) is a promising distributed framework for collaborative artificial
intelligence model training while protecting user privacy. A bootstrapping component that …

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep
learning model training over a large volume of decentralized data residing in mobile devices …

Federated learning (FL) has emerged as a promising paradigm that enables clients to
collaboratively train a shared global model without uploading their local data. To alleviate …

Federated learning (FL) is an on-device distributed learning scheme that does not require
training devices to transfer their data to a centralized facility. The goal of federated learning …