With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …

As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

We consider a federated learning framework in which a parameter server (PS) trains a
global model by using n clients without actually storing the client data centrally at a cloud …

In this work, we present a federated version of the state-of-the-art Neural Collaborative
Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

Traditional federated learning algorithms impose strict requirements on the participation
rates of devices, which limit the potential reach of federated learning. This paper extends the …

Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …

One of the major open problems in sensor-based Human Activity Recognition (HAR) is the
scarcity of labeled data. Among the many solutions to address this challenge, semi …

Emerging machine learning (ML) technologies, in combination with the increasing
computational power of mobile devices, lead to the extensive adoption of ML-based …

As a promising distributed technology, federated learning (FL) has been widely used in
vehicular networks involving large amounts of IoT-enabled sensor data, which derives …