Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …

Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …

Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …

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 …

We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested
recently to reduce the communication overhead of FL due to the repeated transmissions of …

We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile
users (MUs) aim to reach a consensus on a global model with the help of a parameter server …

Federated learning (FL) is an emerging distributed training scheme where edge devices
collaboratively train a model by uploading model updates instead of private data. To …

Federated learning is a distributed framework for training a machine learning model over the
data stored by wireless devices. A major challenge in doing so is the communication …

Federated learning (FL) over wireless communication channels, specifically, over-the-air
(OTA) model aggregation framework is considered. In OTA wireless setups, the adverse …

Federated learning (FL) as a promising edge-learning framework can effectively address the
latency and privacy issues by featuring distributed learning at the devices and model …