CyberDataLab · University of Murcia
Ph.D. Researcher in Federated Learning and CybersecurityCyberDataLab · University of Murcia

Enrique Tomás Martínez Beltrán

Ph.D. student at the University of Murcia working at the intersection of federated learning, cybersecurity, and privacy-preserving AI for real-world systems.

7+

Years of applied research experience

4

EU and defense funded programs

PhD

Candidate at the University of Murcia

880+

Citations

Enrique Tomás Martínez Beltrán
Location

Murcia, Spain

Contact

enriquetomas@um.es

Academic & Professional Profiles

Ph.D. Researcher in Federated Learning and Cybersecurity

Academic and professional profiles to follow scientific output, research identity, and technical activity.

Affiliation

CyberDataLab · University of Murcia

Latest Research Projects

Cutting-edge initiatives in federated learning, cybersecurity, and mission-critical AI systems.

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DEFENDIS: Decentralized Federated Learning for IoT Device Identification and Security
Apr 2023 — Nov 2023Completed

DEFENDIS: Decentralized Federated Learning for IoT Device Identification and Security

DEFENDIS develops a framework for uniquely identifying IoT devices in a distributed manner while solving security threats through decentralized federated learning.

Federated LearningIoTCybersecurity
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EU-GUARDIAN: European Framework and Proofs-of-concept for the Intelligent Automation of Cyber Defence Incident Management
Dec 2022 — Nov 2025Completed

EU-GUARDIAN: European Framework and Proofs-of-concept for the Intelligent Automation of Cyber Defence Incident Management

A cutting-edge AI-based solution for automating cyber defence incident management processes, enhancing EU cyber defence posture and operational capabilities.

AICyber DefenceAutomation
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Recent Publications

Selected highlights from ongoing research in decentralized federated learning, communications security, and trustworthy AI.

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Journal article2026Computer 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 enforce rigid, locks...

Research Notes & Essays

Technical writing on federated learning, decentralized AI, and applied cybersecurity.

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NEBULA: A Platform for Decentralized Federated Learning
March 13, 202512 min read

NEBULA: A Platform for Decentralized Federated Learning

This comprehensive guide explores NEBULA, a revolutionary platform for decentralized federated learning, from basic concepts to advanced applications in healthcare, IoT, and cybersecurity, with practical implementation examples.

Federated LearningAIPrivacyDecentralized SystemsNEBULA
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Federated Learning: Revolutionizing AI Without Compromising Privacy
January 29, 20248 min read

Federated Learning: Revolutionizing AI Without Compromising Privacy

Explore how federated learning is transforming the AI landscape by enabling collaborative model training without sharing raw data, preserving privacy while advancing machine learning capabilities.

Federated LearningPrivacyAIDistributed SystemsCybersecurity
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Direct email

Contact

I'm open to research partnerships, collaborations, and opportunities in federated learning, cybersecurity, and privacy-preserving AI.

enriquetomas@um.es

Affiliation

CyberDataLab · University of Murcia

Location

Murcia, Spain

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enriquetomas@um.es