5 papers
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…
Verifiable Split Learning via zk-SNARKs
Rana Alaa, DarÃo González-Ferreiro, Carlos Beis-Penedo +3
Split learning is an approach to collaborative learning in which a deep neural network is divided into two parts: client-side and server-side at a cut layer. The client side execut…
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric
Carlos Beis Penedo, Rebeca P. DÃaz Redondo, Ana Fernández Vilas +2
Collaborative machine learning in sensitive domains demands scalable, privacy preserving solutions for enterprise deployment. Conventional Federated Learning (FL) relies on a centr…
A Blockchain Solution for Collaborative Machine Learning over IoT
Carlos Beis-Penedo, Francisco Troncoso-Pastoriza, Rebeca P. DÃaz-Redondo +3
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the…
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…