179 citations · 347 across the 13 of their papers we have counts for
15 papers
AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
Ignacio Heredia, Álvaro López García, Fernando Aguilar Gómez +28
The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique re…
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
Judith Sáinz-Pardo Díaz, Andreas Athanasiou, Kangsoo Jung +2
Federated learning is a distributed learning technique that allows training a global model with the participation of different data owners without the need to share raw data. This…
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data
Judith Sáinz-Pardo Díaz, Álvaro López García
The development of deep learning techniques is a leading field applied to cases in which medical data is used, particularly in cases of image diagnosis. This type of data has priva…
An Open Source Python Library for Anonymizing Sensitive Data
Judith Sáinz-Pardo Díaz, Álvaro López García
Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements…
Comparison of machine learning models applied on anonymized data with different techniques
Judith Sáinz-Pardo Díaz, Álvaro López García
Anonymization techniques based on obfuscating the quasi-identifiers by means of value generalization hierarchies are widely used to achieve preset levels of privacy. To prevent dif…
A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters
Jose González-Abad, Álvaro López García, Valentin Y. Kozlov
Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be e…