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 …

Recent years have witnessed the widespread popularity of Internet of things (IoT). By
providing sufficient data for model training and inference, IoT has promoted the development …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Emerging applications such as Internet of Everything, Holographic Telepresence,
collaborative robots, and space and deep-sea tourism are already highlighting the …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Abstract The Internet of Things (IoT) allows communication between devices, things, and
any digital assets that send and receive data over a network without requiring interaction …

Existing approaches to federated learning suffer from a communication bottleneck as well as
convergence issues due to sparse client participation. In this paper we introduce a novel …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …