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; José Antonio Ruiz-Millán; Eugenio Martínez-Cámara; Gerardo González-Seco; María Victoria Luzón; Miguel Angel Veganzones; Francisco Herrera (Inf. Fusion, 2020)

Can we infer the presence of Differential Privacy in Deep Learning models' weights? Towards more secure Deep Learning. - Daniel Jiménez-López; Nuria Rodríguez Barroso; María Victoria Luzón; Francisco Herrera (CoRR, 2023)

Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges. - Nuria Rodríguez Barroso; Daniel Jiménez-López; María Victoria Luzón; Francisco Herrera; Eugenio Martínez-Cámara (Inf. Fusion, 2023)

A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends. - María Victoria Luzón; Nuria Rodríguez Barroso; Alberto Argente-Garrido; Daniel Jiménez-López; Jose M. Moyano; Javier Del Ser; Weiping Ding 001; Francisco Herrera (IEEE CAA J. Autom. Sinica, 2024)

FLEX: Flexible Federated Learning Framework. - Francisco Herrera; Daniel Jiménez-López; Alberto Argente-Garrido; Nuria Rodríguez Barroso; Cristina Zuheros; Ignacio Aguilera-Martos; Beatriz Bello; Mario García-Márquez; María Victoria Luzón (Inf. Fusion, 2025)

Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity. - Daniel Jiménez-López; Nuria Rodríguez Barroso; María Victoria Luzón; Francisco Herrera (CoRR, 2025)

The Population Synthesis Toolkit (PST) Python Library. - Pablo Corcho-Caballero; Yago Ascasibar; Daniel Jiménez-López (J. Open Source Softw., 2025)