In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

The intersection of Federated Learning (FL) and Healthcare 5.0 promises a transformative
shift towards a more resilient future, particularly concerning pandemic preparedness. Within …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

Federated edge learning (FEEL) technology for vehicular networks is considered as a
promising technology to reduce the computation workload while kee** the privacy of …

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine
Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments …

We study federated learning (FL) with over-the-air aggregation over time-varying wireless
channels. Independent workers compute local gradients based on their local datasets and …

As the smart grid develops rapidly, abundant connected devices offer various trading data.
This raises higher requirements for secure and effective data storage. Traditional centralized …

Federated Learning (FL) is an intriguing distributed machine learning approach due to its
privacy-preserving characteristics. To balance the trade-off between energy and execution …

With the rapid development of wireless sensor networks (WSNs) and the Internet of Things
(IoT), increasing computing tasks are sinking to mobile edge networks, such as distributed …