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 …

Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

Federated learning (FL) enables clients learning a shared global model from multiple
distributed devices while keeping training data locally. Due to the synchronous update mode …

In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …

Federated learning (FL) is a promising distributed learning mechanism that revolutionizes
our interaction with data in the IoT ecosystem. Due to the rapidly growing scale of smart …

As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …

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

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …

Federated learning (FL) has recently become one of the hottest focuses in wireless edge
networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …