Federated learning (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

In existing federated learning (FL), the base station (BS) coordinates devices to
collaboratively train a shared model by avoiding the transmission of raw data. To achieve …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

Federated learning (FL) provides a privacy-preserving approach to realizing networked
intelligence. However, the performance of FL is often constrained by the limited …

Motivated by increasing computational capabilities of wireless devices, as well as
unprecedented levels of user-and device-generated data, new distributed machine learning …

Federated learning (FL) is a framework for distributed learning of centralized models. In FL,
a set of edge devices train a model using their local data, while repeatedly exchanging their …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …

Federated learning (FL) has achieved great success as a privacy-preserving distributed
training paradigm, where many edge devices collaboratively train a machine learning model …

Federated learning (FL) is recognized as a promising privacy-preserving distributed
machine learning paradigm, given its potential to enable collaborative model training among …

Federated learning (FL) enables distributed learning across edge devices while protecting
data privacy. However, the learning accuracy decreases due to the heterogeneity of devices' …