In recent years, decentralized machine learning has emerged as a significant advancement
in biomedical applications, offering robust solutions for data privacy, security, and …

Artificial intelligence (AI) has the potential to transform clinical practice and healthcare.
Following impressive advancements in fields such as computer vision and medical imaging …

The authors introduce a novel framework that integrates federated learning with Graph
Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop …

Background The integrity and reliability of clinical research outcomes rely heavily on access
to vast amounts of data. However, the fragmented distribution of these data across multiple …

Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based
machine learning. In FL, the sensitive data remains in data silos and only aggregated …

Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct
rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are …

Federated learning provides the solution when multiple parties want to collaboratively train a
machine learning model without directly sharing sensitive data. In Federated Learning, each …

Batch effects in omics data obscure true biological signals and constitute a major challenge
for privacy-preserving analyses of distributed patient data. Existing batch effect correction …

Background: Central collection of distributed medical patient data is problematic due to strict
privacy regulations. Especially in clinical environments, such as clinical time-to-event …

Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous
quantification of thousands of proteins. Pooling patient-derived data from multiple institutions …