Despite significant improvements over the last few years, cloud-based healthcare
applications continue to suffer from poor adoption due to their limitations in meeting stringent …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Digital therapeutics, evidence-based treatments delivered through software programs, are
revolutionizing healthcare by utilizing cutting-edge computing technologies. Unlike …

In this study, a weighted federated learning approach is proposed for electrocardiogram
(ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data …

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

Since the 20th century, cancer has been a growing threat to human health. Cancer is a
malignant tumor with high clinical morbidity and mortality, and there is a high risk of …

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare,
integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare …

Ensuring the transparency of machine learning models is vital for their ethical application in
various industries. There has been a concurrent trend of distributed machine learning …

Diabetic retinopathy (DR) is a primary cause of impaired vision that can lead to permanent
blindness if not detected and treated early. Unfortunately, DR frequently has no early …

Federated Learning (FL) has emerged as a promising approach to address privacy
concerns inherent in Machine Learning (ML) practices. However, conventional FL methods …