In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with
edge computing is a promising area with novel applications over mobile edge devices. In …

The rapid growth in storage capacity and computational power of mobile devices is making it
increasingly attractive for devices to process data locally instead of risking privacy by …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

ML has been increasingly adopted in wireless communications, with popular techniques,
such as supervised, unsupervised, and reinforcement learning, applied to traffic …

With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

Wireless communications are envisioned to bring about dramatic changes in the future, with
a variety of emerging applications, such as virtual reality, Internet of Things, and so on …

In this paper, we consider federated learning in wireless edge networks. Transmitting
stochastic gradients (SG) or deep model's parameters over a limited-bandwidth wireless …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient federated learning at the wireless network edge, with latency and learning …