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Enrique Tomás Martínez Beltrán

Federated learning, trustworthy AI and cyberdefense research, focused on systems that are robust, privacy-preserving and useful in security operations.

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  1. Home
  2. Publications on Federated Learning, AI and Cybersecurity
  3. COnVIDa: COVID-19 multidisciplinary data collection and dashboard
Journal article2021

Journal of Biomedical Informatics

COnVIDa: COVID-19 multidisciplinary data collection and dashboard

Since the first reported case in Wuhan in late 2019, COVID-19 has rapidly spread worldwide, dramatically impacting the lives of millions of citizens. To deal with the severe crisis resulting from the pandemic, worldwide...

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COVID-19 pandemicSARS-CoV-2DashboardData gatheringData visualization

Abstract

Since the first reported case in Wuhan in late 2019, COVID-19 has rapidly spread worldwide, dramatically impacting the lives of millions of citizens. To deal with the severe crisis resulting from the pandemic, worldwide institutions have been forced to make decisions that profoundly affect the socio-economic realm. In this sense, researchers from diverse knowledge areas are investigating the behavior of the disease in a rush against time. In both cases, the lack of reliable data has been an obstacle to carry out such tasks with accuracy. To tackle this challenge, COnVIDa (https://convida.inf.um.es) has been designed and developed as a user-friendly tool that easily gathers rigorous multidisciplinary data related to the COVID-19 pandemic from different data sources. In particular, the pandemic expansion is analyzed with variables of health nature, but also social ones, mobility, etc. Besides, COnVIDa permits to smoothly join such data, compare and download them for further analysis. Due to the open-science nature of the project, COnVIDa is easily extensible to any other region of the planet. In this way, COnVIDa becomes a data facilitator for decision-making processes, as well as a catalyst for new scientific researches related to this pandemic.

Authors

Enrique Tomás Martínez BeltránMario Quiles PérezJavier Pastor-GalindoPantaleone NespoliFélix Jesús García ClementeFélix Gómez Mármol

Keywords

COVID-19 pandemicSARS-CoV-2DashboardData gatheringData visualization

Related publications

Works with stronger overlap in topic, type, and tags.

Journal article2023

Neural Computing and Applications

Analyzing the impact of Driving tasks when detecting emotions through brain--computer interfaces

Mario Quiles Pérez, Enrique Tomás Martínez Beltrán, Sergio López Bernal, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Traffic accidents are the leading cause of death among young people, a problem that today costs an enormous number of victims. Several technologies have been proposed to prevent accidents, being brain--computer interface...

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Journal article2024

Applied Intelligence

Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Enrique Tomás Martínez Beltrán, Daniel Demeter, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller

Federated learning (FL) enables participants to collaboratively train machine and deep learning models while safeguarding data privacy. However, the FL paradigm still has drawbacks that affect its trustworthiness, as mal...

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Journal article2026

Computer Networks

Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning

Enrique Tomás Martínez Beltrán, Eduard Gash, Gérôme Bovet, Alberto Huertas Celdrán, Burkhard Stiller

Decentralized Federated Learning (DFL) offers a scalable paradigm for collaborative intelligence at the edge, yet its practical efficacy is severely constrained by system heterogeneity. Traditional synchronous protocols...

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