5 citations · 9 across the 5 of their papers we have counts for
5 papers
Coded Task Offloading for Fluid Computing: A Privacy-Aware Approach under D2D Networks
Diego Cajaraville-Aboy, Manuel Fernández-Veiga, Ana Fernández-Vilas +1
Fluid Computing aims to support distributed applications execution across heterogeneous cloud, edge, and device resources, motivating task execution mechanisms that adapt to dynami…
Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +2
Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different ad…
Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator
Alberto Bazán-Guillén, Carlos Beis-Penedo, Diego Cajaraville-Aboy +6
Realistic urban traffic simulation is essential for sustainable urban planning and the development of intelligent transportation systems. However, generating high-fidelity, time-va…
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo +5
The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network security, as it allows malicious…
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +1
Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learnin…