Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Veiga, Manuel"'
Autor:
Fernández-Piñeiro, Pablo, Ferández-Veiga, Manuel, Díaz-Redondo, Rebeca P., Fernández-Vilas, Ana, González-Soto, Martín
In prototype-based federated learning, the exchange of model parameters between clients and the master server is replaced by transmission of prototypes or quantized versions of the data samples to the aggregation server. A fully decentralized deploym
Externí odkaz:
http://arxiv.org/abs/2411.09267
Autor:
Castro, David Pérez, Vilas, Ana Fernández, Fernández-Veiga, Manuel, Rodríguez, Mateo Blanco, Redondo, Rebeca P. Díaz
We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not curr
Externí odkaz:
http://arxiv.org/abs/2410.09779
Autor:
Cajaraville-Aboy, Diego, Fernández-Vilas, Ana, Díaz-Redondo, Rebeca P., Fernández-Veiga, Manuel
Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, the
Externí odkaz:
http://arxiv.org/abs/2409.17754
Autor:
Soler, David, Dafonte, Carlos, Fernández-Veiga, Manuel, Vilas, Ana Fernández, Nóvoa, Francisco J.
Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such that the secret is modified in member insertions and deletions. Althoug
Externí odkaz:
http://arxiv.org/abs/2405.12042
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 so, FL has some privacy vulnerabilities that have been tried to be ov
Externí odkaz:
http://arxiv.org/abs/2405.01704
Autor:
Soler, David, Cillero, Iván, Dafonte, Carlos, Fernández-Veiga, Manuel, Fernández-Vilas, Ana, Nóvoa, Francisco J.
The first Quantum Key Distribution (QKD) networks are currently being deployed, but the implementation cost is still prohibitive for most researchers. As such, there is a need for realistic QKD network simulators. The \textit{QKDNetSim} module for th
Externí odkaz:
http://arxiv.org/abs/2402.10822
Autor:
Soler, David, Dafonte, Carlos, Fernández-Veiga, Manuel, Vilas, Ana Fernández, Nóvoa, Francisco J.
High-entropy random numbers are an essential part of cryptography, and Quantum Random Number Generators (QRNG) are an emergent technology that can provide high-quality keys for cryptographic algorithms but unfortunately are currently difficult to acc
Externí odkaz:
http://arxiv.org/abs/2401.16170
Autor:
González-Soto, Martín, Díaz-Redondo, Rebeca P., Fernández-Veiga, Manuel, Rodríguez-Castro, Bruno, Fernández-Vilas, Ana
Publikováno v:
Computer Networks. Volume 239, 2024
Decentralised machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralised and collaborative machine learning framework specially or
Externí odkaz:
http://arxiv.org/abs/2312.12190
Publikováno v:
Computer Networks, 234, 109921, 2023
Irregular repetition slotted Aloha (IRSA) has shown significant advantages as a modern technique for uncoordinated random access with massive number of users due to its capability of achieving theoretically a throughput of $1$ packet per slot. When t
Externí odkaz:
http://arxiv.org/abs/2312.06516
Autor:
Beis-Penedo, Carlos, Troncoso-Pastoriza, Francisco, Díaz-Redondo, Rebeca P., Fernández-Vilas, Ana, Fernández-Veiga, Manuel, Soto, Martín González
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 challenges associated with data privacy, security, and scalability. Fe
Externí odkaz:
http://arxiv.org/abs/2311.14136