Vertical federated learning (FL) is a critical tech-nology to support distributed artificial
intelligence (AI) services in futuristic 6G systems, since it enables efficient and secure …

The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …

Federated learning (FL) over wireless communication channels, specifically, over-the-air
(OTA) model aggregation framework is considered. In OTA wireless setups, the adverse …

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively
train deep learning models from the distributed data in 6G networks. Nevertheless, the …

Under the federated learning paradigm, a set of nodes can cooperatively train a machine
learning model with the help of a centralized server. Such a server is also tasked with …

Over-the-air federated learning (OTA-FL) integrates communication and model aggregation
by exploiting the innate superposition property of wireless channels. The approach renders …

Federated learning (FL) is a promising technology which trains a machine learning model
on edge devices in a distributed manner orchestrated by a parameter server (PS). To realize …

Traditional federated learning methods assume that users have fully labeled data in their
device for training, but in practice, labels are difficult to obtain due to various reasons such …

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …

This paper presents the first broadband digital over-the-air computation (AirComp) system
for phase asynchronous OFDM-based federated edge learning systems. Existing analog …