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 …

Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …

Since traditional federated learning (FL) algorithms cannot provide sufficient privacy
guarantees, an increasing number of approaches apply local differential privacy (LDP) …

The existing federated learning framework is based on the centralized model coordinator,
which still faces serious security challenges such as device differentiated computing power …

Federated learning (FL) is widely used in edge environments as a privacy-preserving
collaborative learning paradigm. However, edge devices often have heterogeneous …

In this paper, we propose an adaptive learning paradigm for resource-constrained cross-
device federated learning, in which heterogeneous local submodels with varying resources …

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in
which wireless client-devices independently train their ML models and send the locally …

The emerging federated learning is a distributed machine learning paradigm which enables
training a global model on a massive number of edge devices while protecting the privacy of …

The emerging Personalized Federated Learning (PFL) methods aim to produce
personalized models for different users, so as to keep track of their individualized …