To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

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 …

Although Federated learning (FL) over wireless medium is a promising technology, a large
number of concurrent FL tasks, generated by the urgent demand for ubiquitous intelligence …

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great
attention due to the outstanding performance in improving communication efficiency for …

Over-the-air Computation (AirComp) has been demonstrated as an effective transmission
scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL …

Over-the-air federated learning (FL) is a recent paradigm to address the communication
bottleneck of FL, where a machine learning model is trained by aggregating the local …

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly
prominent isolated data islands problem while keeping users' data locally with privacy and …

The fundamental communication paradigms in the next-generation mobile networks are
shifting from connected things to connected intelligence. The potential result is that current …

In a multi-agent system, agents can cooperatively learn a model from data by exchanging
their estimated model parameters, without the need to exchange the locally available data …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …