With the rapid development of enabling technologies like VR and AR, we human beings are
on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to
train models without sharing users' private data. In this paper, we introduce a decentralized …

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

Federated learning (FL) encounters two major obstacles, namely the heterogeneity among
Internet of Things (IoT) devices hampers both personalized and generalized performance …

Despite the advantages of fog-assisted internet of drones (IoD) networks for federated
learning (FL) model aggregations, it is restricted by the limited battery capacity of drones and …

Recent years have witnessed the rapid development of large language models (LLMs) in
various domains. To better serve the large number of Chinese users, many commercial …

It is anticipated that aerial-terrestrial integrated networks incorporating unmanned aerial
vehicles (UAVs) mounted relays will offer improved coverage and connectivity in the beyond …

Federated Learning (FL) has been widely adopted to process the enormous data in the
application scenarios like Edge Computing (EC). However, the commonly-used …

In large-scale federated and distributed learning, communication efficiency is one of the
most challenging bottlenecks. While gossip communication—where agents can exchange …