294 citations · 352 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
RAB-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients
Nuria Rodríguez-Barroso, M. Victoria Luzón, Francisco Herrera
At the same time that artificial intelligence is becoming popular, concern and the need for regulation is growing, including among other requirements the data privacy. In this cont…
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…