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 …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …

The development of AI applications, especially in large-scale wireless networks, is growing
exponentially, alongside the size and complexity of the architectures used. Particularly …

This work presents AttFL, a federated learning framework designed to continuously improve
a personalized deep neural network for efficiently analyzing time-series data generated from …

Federated learning (FL) is a distributed machine learning paradigm that enables data
owners to collaborate on training models while preserving data privacy. As FL effectively …

The field of mobile and wearable computing is undergoing a revolutionary integration of
machine learning. Devices can now diagnose diseases, predict heart irregularities, and …

Federated learning (FL) has been proposed as a privacy-preserving distributed learning
paradigm, which differs from traditional distributed learning in two main aspects: the systems …