Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

With the increasing amount of multimedia data on modern mobile systems and IoT
infrastructures, harnessing these rich multimodal data without breaching user privacy …

Advances in the new generation of Internet of Things (IoT) technology are propelling the
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …

While federated learning (FL) systems often utilize quantization to battle communication and
computational bottlenecks they have heretofore been limited to deploying fixed-precision …

Federated learning (FL) enables geographically dispersed edge devices (ie, clients) to learn
a global model without sharing the local datasets, where each client performs gradient …

Federated learning (FL) opens a new promising paradigm for the Industrial Internet of
Things (IoT) since it can collaboratively train machine learning models without sharing …

We study asynchronous federated learning mechanisms with nodes having potentially
different computational speeds. In such an environment, each node is allowed to work on …

Nowadays, the training data and neural network models are getting increasingly large. The
training time of deep learning will become unbearably long on a single machine. To reduce …

Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …