
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.
Ph.D. student at the University of Murcia working at the intersection of federated learning, cybersecurity, and privacy-preserving AI for real-world systems.
Years of applied research experience
EU and defense funded programs
Candidate at the University of Murcia
Citations

Murcia, Spain
enriquetomas@um.es
Academic and professional profiles to follow scientific output, research identity, and technical activity.
Affiliation
CyberDataLab · University of Murcia
Cutting-edge initiatives in federated learning, cybersecurity, and mission-critical AI systems.

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

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I'm open to research partnerships, collaborations, and opportunities in federated learning, cybersecurity, and privacy-preserving AI.
enriquetomas@um.esAffiliation
CyberDataLab · University of Murcia
Location
Murcia, Spain