Publications on Federated Learning, AI and Cybersecurity

SYNAPSE: Framework for neuron analysis and perturbation in sequence encoding

Jesús Sánchez Ochoa, Enrique Tomás Martínez Beltrán, Alberto Huertas Celdrán

SYNAPSE analyzes Transformer neurons and stress-tests internal robustness through controlled interventions during inference, without retraining the base model.

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

CAFF addresses delays and unequal participation in asynchronous DFL. A cache with one slot per neighbour, staleness filtering and an adaptive threshold balance training efficiency and predictive quality.

Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data

Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Modalis addresses clients with different or missing modalities in decentralized learning. It exchanges compact class prototypes and aligns local representations to support collaboration across heterogeneous data.

Decentralized Self-Supervised Representation Learning via Prototype Exchange under Non-IID Data

Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

This manuscript studies self-supervised representation learning through prototype exchange between peers with non-IID data. It has been submitted to Future Generation Computer Systems.

FedEnD: Communication-efficient Federated Learning for non-IID data via decentralized ensemble distillation

Enrique Tomás Martínez Beltrán, Philip Giryes, Gérôme Bovet, Burkhard Stiller, Gregorio Martínez Pérez, Alberto Huertas Celdrán

FedEnD addresses heterogeneous client data and communication costs in federated learning. Clients train local specialists, exchange them once and distil a weighted ensemble using their own data without an auxiliary public dataset.

MadHoney: Señuelos Tóxicos para la Defensa Activa en el Aprendizaje Federado Descentralizado

Pedro Beltrán López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

RepuNet: A Reputation System for Mitigating Malicious Clients in DFL

Isaac Marroqui Penalva, Enrique Tomás Martínez Beltrán, Manuel Gil Pérez, Alberto Huertas Celdrán

RepuNet studies how to reduce the influence of malicious DFL clients. It combines model and communication signals into a reputation score that adjusts each peer's weight during aggregation, with an evaluation in NEBULA.

TemporalFED: A Software Module for Detecting Cyberattacks in Industry 4.0 Time-Series Data Using Decentralized Federated Learning

Ángel Luis Perales Gómez, Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán

Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues, such as cyberattacks affecting industrial processes. Federated Le...

Aprendizaje federado descentralizado y conciencia situacional para el reconocimiento aéreo militar seguro y resiliente

Enrique Tomás Martínez Beltrán, Miguel Fernández Llamas, Anas Zine Boujemaoui, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

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...

NEBULA - Decentralized Federated Learning for Heterogeneous Networks

Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Federated learning (FL) enables collaborative model training without sharing raw data, which is pivotal for maintaining privacy. However, existing FL frameworks often rely on a central coordinator, posing risks in het...

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

Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Miguel Fernández Llamas, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Chao Feng, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Decentralized Federated Learning (DFL) enables collaborative, privacy-preserving model training without relying on a central server. This decentralized approach reduces bottlenecks and eliminates single points of fail...

When Brain–Computer Interfaces meet the metaverse: Landscape, demonstrator, trends, challenges, and concerns

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

The metaverse has gained tremendous popularity in recent years, allowing the interconnection of users worldwide. However, current systems in metaverse scenarios, such as virtual reality glasses, offer a partial immers...

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...

DART: A Solution for decentralized federated learning model robustness analysis

Chao Feng, Alberto Huertas Celdrán, Jan von der Assen, Enrique Tomás Martínez Beltrán, Gérôme Bovet, Burkhard Stiller

Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (C...

Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges

Mario Quiles Pérez, Enrique Tomás Martínez Beltrán, Sergio López Bernal, Eduardo Horna Prat, Luis Montesano Del Campo, Lorenzo Fernández Maimó, Alberto Huertas Celdrán

The primary goal of any company is to increase its profits by improving both the quality of its products and how they are advertised. In this context, neuromarketing seeks to enhance the promotion of products and gene...

Fedstellar: A Platform for Decentralized Federated Learning

Enrique Tomás Martínez Beltrán, Ángel Luis Perales Gómez, Chao Feng, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) h...

Mitigación de ataques bizantinos usando modelos históricos en aprendizaje federado descentralizado

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

El Aprendizaje Federado Descentralizado emerge como una solución prometedora para entrenar modelos de inteligencia artificial de manera colaborativa, sin compartir directamente los datos y sin la necesidad de un servi...

Mitigating communications threats in decentralized federated learning through moving target defense

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

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. Howe...

NeuronLab: BCI framework for the study of biosignals

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

Brain–Computer Interfaces (BCIs) allow the acquisition of brain activity using non-invasive techniques such as Electroencephalography (EEG). Since BCI devices do not commonly interpret the acquired EEG signals, extern...

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

Sergio López Bernal, José Manuel Hidalgo Rogel, Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán

In response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify...

Reputation System based on Distributed Ledger to Secure Decentralized Federated Learning

Jan von der Assen, Sandrin Raphael Hunkeler, Alberto Huertas Celdrán, Enrique Tomás Martínez Beltrán, Gérôme Bovet, Burkhard Stiller

Machine Learning (ML) faces several challenges, including susceptibility to data leakage and the overhead associated with data storage. Decentralized Federated Learning (DFL) offers a robust solution to these issues b...

Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

Chao Feng, Alberto Huertas Celdrán, Janosch Baltensperger, Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Gérôme Bovet, Burkhard Stiller

Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography

José Manuel Hidalgo Rogel, Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Sergio López Bernal, Gregorio Martínez Pérez, Alberto Huertas Celdrán

Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers' drowsiness using Brain-Comp...

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 interf...

Decentralized Federated Learning: Enabling Collaborative AI With Enhanced Trust and Efficiency

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

Conference tutorial on decentralized federated learning: foundations, architectures, security, privacy and practical experimentation, presented at ECAI 2023.

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,...

Fedstellar: a platform for training models in a privacy-preserving and decentralized fashion

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

This paper presents Fedstellar, a platform for training decentralized Federated Learning (FL) models in heterogeneous topologies in terms of the number of federation participants and their connections. Fedstellar allo...

Framework Seguro para Entrenar Modelos de Inteligencia Artificial Federados y Descentralizados

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

Opportunities for standardization in emergency scenarios in the European Union

Sergio López Bernal, Mario Quiles Pérez, Enrique Tomás Martínez Beltrán, María del Carmen Martín Curto, Yantsislav Yanakiev, Manuel Gil Pérez, Gregorio Martínez Pérez

Objective Despite current standardization actions towards the unification between European Union (EU) countries, there is still much work to do. In this context, this paper aims to offer a comprehensive analysis of th...

Stealth Spectrum Sensing Data Falsification Attacks Affecting IoT Spectrum Monitors on the Battlefield

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

A Review of Noise-based Cyberattacks Generating Fake P300 Waves in Brain-Computer Interfaces

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

Noise-based cyberattacks generating fake P300 waves in brain--computer interfaces

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

Most of the current Brain--Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject's information. It means that if EEG were maliciously manipulated, the proper...

SAFECAR: A Brain–Computer Interface and intelligent framework to detect drivers’ distractions

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

As recently reported by the World Health Organization (WHO), the high use of intelligent devices such as smartphones, multimedia systems, or billboards causes an increase in distraction and, consequently, fatal accide...

SecBrain: A Framework to Detect Cyberattacks Revealing Sensitive Data in Brain-Computer Interfaces

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

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

Breaching Subjects’ Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces

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

Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the...

COnVIDa: COVID-19 multidisciplinary data collection and dashboard

Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Javier Pastor-Galindo, Pantaleone Nespoli, Félix Jesús García Clemente, Félix Gómez Mármol

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, worldwi...