4 papers
Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies
Astrid Brull, Sara Aguti, Véronique Bolduc +9
The application of Machine Learning (ML) to the diagnosis of rare diseases, such as collagen VI-related dystrophies (COL6-RD), is fundamentally limited by the scarcity and fragment…
Federated Learning for Pediatric Pneumonia Detection: Enabling Collaborative Diagnosis Without Sharing Patient Data
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Joaquin Del Rio +2
Early and accurate pneumonia detection from chest X-rays (CXRs) is clinically critical to expedite treatment and isolation, reduce complications, and curb unnecessary antibiotic us…
Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Joaquin Del Rio +2
Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Train…
The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications
Alex Acero, Daniel M. Jimenez-Gutierrez, Dario Pighin +3
Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data private. There are two main paradigms in FL: Horizontal…