activity
20202026
most citedFederated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy

149 citations · 153 across the 8 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.AI2025★ 2 cited

From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0

Nuria Rodríguez-Barroso, Mario García-Márquez, M. Victoria Luzón +1

Federated Learning (FL) enables privacy-preserving collaborative learning, yet deployments increasingly show that privacy guarantees alone do not sustain trust in high-risk setting…

cs.LG2025

Improving -Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distance

Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón +1

The rapid development of artificial intelligence systems has amplified societal concerns regarding their usage, necessitating regulatory frameworks that encompass data privacy. Fed…

cs.CR2025

Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity

Daniel Jiménez-López, Nuria Rodríguez-Barroso, M. Victoria Luzón +1

Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. Membership Inference Attacks (MIA) exploit it to obt…

cs.LG2025★ 2 cited

Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning

Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón +1

Federated Learning presents a nascent approach to machine learning, enabling collaborative model training across decentralized devices while safeguarding data privacy. However, its…