5 papers · 1 filter
Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3
Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environmen…
Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3
The recent success of large language models (LLMs) has been largely driven by vast public datasets. However, the next frontier for LLM development lies beyond public data. Much of…
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…
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…