Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

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 …

A decentralised patient-centric paradigm is gradually replacing the traditional hospital and
specialist-focused healthcare model. Communication technologies have made it possible to …

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

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

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

The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-
attacks. To prevent and mitigate malicious network activities in a distributed metaverse …

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 …

Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

To fully exploit enormous data generated by intelligent devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …