In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

In the ever-evolving landscape of artificial intelligence (AI) and large language models
(LLMs), handling and leveraging data effectively has become a critical challenge. Most state …

Federated learning facilitates the collaborative learning of a global model across multiple
distributed medical institutions without centralizing data. Nevertheless the expensive cost of …

The non-parallel disease progression and curative effect are the difficulties of clinical
diagnosis and treatment decisions. Experts (doctors) constantly summarize these non …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
collaborative training. Under domain skew the current FL approaches are biased and face …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Medical image classification plays a crucial role in computer-aided clinical diagnosis. While
deep learning techniques have significantly enhanced efficiency and reduced costs, the …

With the flourishing development of the Internet of Things (IoT), federated learning has
garnered significant attention as a distributed learning method aimed at preserving the …