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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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  2. Publications on Federated Learning, AI and Cybersecurity
  3. ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes
Conference paper2025

ICC 2025 - IEEE International Conference on Communications

ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes

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TrainingQuantization (signal)CostsFederated learningPrototypesCollaborationData modelsComplexity theoryOptimizationFacesCommunication OptimizationFederated LearningKnowledge DistillationPrototype LearningQuantization

Abstract

Authors

Pedro Miguel Sánchez SánchezEnrique Tomás Martínez BeltránMiguel Fernández LlamasGérôme BovetGregorio Martínez PérezAlberto Huertas Celdrán

Keywords

TrainingQuantization (signal)CostsFederated learningPrototypesCollaborationData modelsComplexity theoryOptimizationFacesCommunication OptimizationFederated LearningKnowledge DistillationPrototype LearningQuantization

Related publications

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

Journal article2023

IEEE Communications Surveys & Tutorials

Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges

Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

In recent years, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature,...

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Journal article2025

IEEE Communications Magazine

Flighter: Decentralized Federated Learning and Situational Awareness for Secure Military Aerial Reconnaissance

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

Mosaic warfare is a military strategy where reconnaissance missions with aerial vehicles are critical for gathering enemy information and achieving battlefield dominance. Nowadays, machine learning (ML) techniques pla...

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Conference paper2022

2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)

Study of P300 Detection Performance by Different P300 Speller Approaches Using Electroencephalography

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

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Related Research

COBRA V2: Gamified and Customizable Hyperrealistic Cyber Range Simulations of APTs

Nov 2025 — Jun 2026

COBRA V2: Gamified and Customizable Hyperrealistic Cyber Range Simulations of APTs

Develops adaptive training environments and realistic Advanced Persistent Threat (APT) simulation tools using gamification mechanics.

DEFENDIS: Decentralized Federated Learning for IoT Device Identification and Security

Apr 2023 — Nov 2023

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

ECYSAP EYE: European Cyber Situational Awareness Platform - Enhanced Cyberspace Operations

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