Machine learning (ML) models have been deployed in mobile networks to deal with massive
data from different layers to enable automated network management. To overcome high …

Federated learning (FL) is a privacy-preserving distributed machine learning technique that
trains models while keeping all the original data generated on devices locally. Since devices …

Federated Learning (FL) is widely used in mobile edge applications. However, the
heterogeneity issues of mobile edge devices pose significant challenges to the …

Federated Learning (FL) has received tremendous attention as a decentralized machine
learning (ML) framework that allows distributed data owners to collaboratively train a global …

Asynchronous federated learning (AFL) is a distributed machine learning technique that
allows multiple devices to collaboratively train deep learning models without sharing local …

Federated learning is an emerging concept that trains the machine learning models with the
distributed datasets, without sending the raw data to the data center. But in an edge …

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) revolutionizes collaborative machine learning among Internet of
Things (IoT) devices by enabling them to train models collectively while preserving data …

Federated Learning (FL) is a distributed learning framework that enables collaborative
model training without raw data sharing. However, due to the shortage of memory resources …

Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to
obtain reliable specialized models when data is imbalanced and distributed in a non-iid …