294 citations · 447 across the 9 of their papers we have counts for
9 papers
Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learning
Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón +1
Federated Learning (FL) has emerged as a key paradigm for building Trustworthy AI systems by enabling privacy-preserving, decentralized model training. However, FL is highly suscep…
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…
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
Qinjun Fei, Nuria Rodríguez-Barroso, María Victoria Luzón +2
In cross-silo Federated Learning (FL), client selection is critical to ensure high model performance, yet it remains challenging due to data quality decompensation, budget constrai…
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…
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…
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…
FLEX: FLEXible Federated Learning Framework
Francisco Herrera, Daniel Jiménez-López, Alberto Argente-Garrido +6
In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and h…
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Nuria Rodríguez-Barroso, Daniel Jiménez López, M. Victoria Luzón +2
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning…
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
Nuria Rodríguez-Barroso, Goran Stipcich, Daniel Jiménez-López +6
The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit those requirem…