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University of Murcia
AI, cybersecurity and federated learning
I research at the intersection of artificial intelligence, cybersecurity and federated learning to build systems that detect threats, protect data and learn in a decentralized, trustworthy way.
Find publications, research identity and technical work across the main academic profiles.
AffiliationCyberDataLabopens in a new tab / University of Murciaopens in a new tab
Recent work on federated learning, cybersecurity and trustworthy AI, with a short note on why each contribution matters.
Journal article / Computer Networks / 2026
Decentralized Federated Learning (DFL) offers a scalable paradigm for collaborative intelligence at the edge, yet its practical efficacy is severely constrained by system hetero...
Journal article / Information Fusion / 2026
Decentralized Federated Learning (DFL) enables collaborative machine learning across numerous devices while avoiding bottlenecks and reliance on a single trusted entity inherent...
Preprint / Submitted to Future Generation Computer Systems / 2026
Three projects linking distributed AI, cyberdefense and trustworthy security research across European and applied settings.
An architectural evolution of the European cyber situational awareness platform into a modular System of Systems to support military missions.
ROBUST-6G studies security mechanisms for 6G systems, including monitoring, secure data management, trustworthy AI services, federated learning, and threat response.
DEFENDIS develops a framework for uniquely identifying IoT devices in a distributed manner while solving security threats through decentralized federated learning.
Short research notes and technical guides on federated learning, threat detection and cyberdefense workflows.
A practical note on how distributed monitoring, DFL models, alert evidence and LLM-based support can fit into a cyberdefense workflow.
A research note on using DFL to turn distributed telemetry, anomalies and trust signals into cyberdefense situational awareness.
Contact
For research collaborations, European projects, invited talks or applied work in secure distributed AI, send a short note with the context.
enriquetomas@um.esA short context helps me route the message quickly. I usually reply within a few working days.