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 …

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 …

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

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 …

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

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

Abstract Personalized Federated Learning (PFL) aims to train machine learning models on
decentralized, heterogeneous data while preserving user privacy. This research survey …

Federated Learning (FL), an emerging distributed machine learning framework that enables
each client to collaboratively train a global model by sharing local knowledge without …

This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML)
fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive …

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