15 citations · 15 across the 6 of their papers we have counts for
4 papers · 1 filter
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal +4
Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purel…
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…
Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4
Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where al…
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…