Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

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

Auction-based federated learning (AFL) is an important emerging category of FL incentive
mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners …

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

Federated learning (FL) has emerged as a promising distributed learning paradigm with an
added advantage of data privacy. With the growing interest in collaboration among data …

Along with the increasing popularity of deep learning (DL) techniques, more and more
Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable …

Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …

Federated learning involves training statistical models over edge devices such as mobile
phones such that the training data are kept local. Federated Learning (FL) can serve as an …

Although Federated Learning (FL) enables global model training across clients without
compromising their raw data, due to the unevenly distributed data among clients, existing …

Recent advancements in multimodal machine learning have empowered the development
of accurate and robust AI systems in the medical domain, especially within centralized …