Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

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

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 …

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

Federated Learning (FL) allows devices to train a global machine learning model without
sharing data. In the context of wireless networks, the inherently unreliable nature of the …

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

Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm,
enables model training in a distributed environment while ensuring user privacy by using …

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 …