We study federated learning (FL) with over-the-air aggregation over time-varying wireless
channels. Independent workers compute local gradients based on their local datasets and …

The main goal of this paper is to survey the influential research of distributed learning
technologies playing a key role in the 6G world. Upcoming 6G technology is expected to …

Decentralized machine learning is a promising emerging paradigm in view of global
challenges of data ownership and privacy. We consider learning of linear classification and …

Federated learning (FL) is an emerging machine learning paradigm that allows to
accomplish model training without aggregating data at a central server. Most studies on FL …

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

Federated Learning (FL) presents a mechanism to allow decentralized training for machine
learning (ML) models inherently enabling privacy preservation. The classical FL is …

Federated learning (FL) enables wireless terminals to collaboratively learn a shared
parameter model while keeping all the training data on devices per se. Parameter sharing …

Federated learning (FL) is a popular distributed learning paradigm, in which a global model
at a server learns private data of clients without data shared among clients or the server. In …

In this paper, the problem of adaptive distributed learning in diffusion networks is
considered. The algorithms are developed within the convex set theoretic framework. More …

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide
range of attention. However, few works have considered the impact of user mobility on the …