Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless
networks, the surging demand for data communications and computing calls for the …

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-
tuned on a downstream task, has emerged as a powerful technique in natural language …

In federated learning, heterogeneity in the clients' local datasets and computation speeds
results in large variations in the number of local updates performed by each client in each …

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 …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …

The idea of federated learning is to collaboratively train a neural network on a server. Each
user receives the current weights of the network and in turns sends parameter updates …

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 …