Catering to the proliferation of Internet of Things devices and distributed machine learning at
the edge, we propose an energy harvesting federated learning (EHFL) framework in this …

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction
model while keeping data locally. However, there are two major research challenges to …

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

In this paper, the deployment of federated learning (FL) is investigated in an energy
harvesting wireless network in which the base station (BS) is equipped with a massive …

Potential environmental impact of machine learning by large-scale wireless networks is a
major challenge for the sustainability of future smart ecosystems. In this paper, we introduce …

Potential environmental impact of machine learning in large-scale wireless networks is a
major challenge for the sustainability of next-generation intelligent systems. Federated …

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 …

Federated learning (FL) has been considered as a promising paradigm for enabling
distributed machine learning (ML) in wireless networks. To address the limited energy …

Federated learning (FL) is a well-regarded distributed machine learning technology that
leverages local computing resources while protecting privacy. The over-the-air (OTA) …

Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …