179 citations · 421 across the 16 of their papers we have counts for
16 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…
Study of the performance and scalability of federated learning for medical imaging with intermittent clients
Judith Sáinz-Pardo Díaz, Álvaro López García
Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects ab…
Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods
María Castrillo, Álvaro López García
Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables canno…
An efficient cloud scheduler design supporting preemptible instances
Álvaro López García, Enol Fernández-del-Castillo, Isabel Campos Plasencia
Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scie…
Efficient image deployment in cloud environments
Álvaro López García, Enol Fernández del Castillo
The biggest overhead for the instantiation of a virtual machine in a cloud infrastructure is the time spent in transferring the image of the virtual machine into the physical node…
Standards for enabling heterogeneous IaaS cloud federations
Álvaro López García, Enol Fernández del Castillo, Pablo Orviz Fernández
Technology market is continuing a rapid growth phase where different resource providers and Cloud Management Frameworks are positioning to provide ad-hoc solutions -in terms of man…
Orchestrating Complex Application Architectures in Heterogeneous Clouds
Miguel Caballer, Sahdev Zala, Álvaro López García +3
Private cloud infrastructures are now widely deployed and adopted across technology industries and research institutions. Although cloud computing has emerged as a reality, it is n…
Enabling rootless Linux Containers in multi-user environments: the udocker tool
Jorge Gomes, Isabel Campos, Emanuele Bagnaschi +6
Containers are increasingly used as means to distribute and run Linux services and applications. In this paper we describe the architectural design and implementation of udocker, a…
Resource provisioning in Science Clouds: Requirements and challenges
Álvaro López García, Enol Fernández-del-Castillo, Pablo Orviz Fernández +2
Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are d…
Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers
Alvaro Lopez Garcia, Lisa Zangrando, Massimo Sgaravatto +8
Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in…
INDIGO-Datacloud: foundations and architectural description of a Platform as a Service oriented to scientific computing
D. Salomoni, I. Campos, L. Gaido +32
In this paper we describe the architecture of a Platform as a Service (PaaS) oriented to computing and data analysis. In order to clarify the choices we made, we explain the featur…
Analysis of Scientific Cloud Computing requirements
Álvaro López García, Enol Fernández del Castillo
While the requirements of enterprise and web applications have driven the development of Cloud computing, some of its key features, such as customized environments and rapid elasti…