11 papers
Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. DÃaz-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…
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…
Purification Strategy Optimization for Entanglement Routing in Quantum Networks
Javier Vecino Peñas, Ana Fernández-Vilas, Rebeca P. DÃaz-Redondo +2
Quantum networks rely on the efficient distribution of entanglement to enable long-distance quantum communication and information processing. A key challenge in these networks is t…
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…
From Physical to Logical: Graph-State-Based Connectivity in Quantum Networks
Mateo M. Blanco, Manuel Fernández-Veiga, Ana Fernández-Vilas +1
Entanglement is a key resource in quantum communication, but bipartite schemes are often insufficient for advanced protocols like quantum secret sharing or distributed computing. G…
Privacy-aware Berrut Approximated Coded Computing for Federated Learning
Xavier MartÃnez Luaña, Rebeca P. DÃaz Redondo, Manuel Fernández Veiga
Federated Learning (FL) is an interesting strategy that enables the collaborative training of an AI model among different data owners without revealing their private datasets. Even…