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

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

Federated learning (FL) enables multiple clients to collaboratively train machine learning
models without revealing their private training data. In conventional FL, the system follows …

To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …

In recent years, multi-objective optimization (MOO) emerges as a foundational problem
underpinning many multi-agent multi-task learning applications. However, existing …

In recent years, federated learning (FL) has emerged as an important distributed machine
learning paradigm to collaboratively learn a global model with multiple clients, while …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Federated Learning (FL) has been becoming a popular interdisciplinary research area in
both applied mathematics and information sciences. Mathematically, FL aims to …

In Healthcare Internet of Things Networks (HIoTNs), safeguarding the sensitivity of patients'
Electric Healthcare Records (EHRs) is imperative, necessitating effective privacy protection …

The rapid advances in federated learning (FL) in the past few years have recently inspired a
great deal of research on this emerging topic. Existing work on FL often assume that clients …