The emerging Federated Learning (FL) permits all workers (eg, mobile devices) to
cooperatively train a model using their local data at the network edge. In order to avoid the …

Edge intelligence has received great attention for the rapid development and wild
application of edge computing and artificial intelligence. As a key technology in edge …

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial
intelligence paradigm, where over-the-air computation enables spectral-efficient model …

With the explosive growth of massive data generated by smart Internet of Things (IoT)
devices, federated learning has been envisioned as a promising technique to provide …

Real-time machine learning (ML) has recently attracted significant interest due to its
potential to support instantaneous learning, adaptation, and decision making in a wide …

In this study, we propose circularly-shifted chirp (CSC)-based majority vote (MV)(CSC-MV),
a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated …

Optimal algorithm design for federated learning (FL) remains an open problem. This paper
explores the full potential of FL in practical edge computing systems where workers may …

Over-the-air federated learning (OTA-FL) exploits the inherent superposition property of
wireless channels to integrate the communication and model aggregation. Though a …

Performing federated learning continuously in edge networks while training data are
dynamically and unpredictably streamed to the devices faces critical challenges, including …

As a practical paradigm designed to involve large numbers of edge devices in distributed
training of deep learning models, federated learning has witnessed a significant amount of …