5 papers
Verifiable Split Learning via zk-SNARKs
Rana Alaa, Darío 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. Díaz 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…
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…
A Blockchain Solution for Collaborative Machine Learning over IoT
Carlos Beis-Penedo, Francisco Troncoso-Pastoriza, Rebeca P. Díaz-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…