Zobrazeno 1 - 10
of 312
pro vyhledávání: '"Vilas, Ana"'
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
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
Autor:
Cerezo-Costas, Héctor, Vilas, Ana Fernández, Martín-Vicente, Manuela, Díaz-Redondo, Rebeca P.
Publikováno v:
Expert Systems with Applications, 2018, vol. 95, p. 32-42
Citizens are actively interacting with their surroundings, especially through social media. Not only do shared posts give important information about what is happening (from the users' perspective), but also the metadata linked to these posts offer r
Externí odkaz:
http://arxiv.org/abs/2312.11076
Publikováno v:
Computer Networks 131, 2018
Ad hoc architectures have emerged as a valuable alternative to centralized participatory sensing systems due to their infrastructureless nature, which ensures good availability, easy maintenance and direct user communication. As a result, they need t
Externí odkaz:
http://arxiv.org/abs/2312.09957
Autor:
Castro-Jul, Fátima, Redondo, Rebeca Díaz, Fernández-Vilas, Ana, Chabridon, Sophie, Conan, Denis
Publikováno v:
Sensors 2019, 19(1)
Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development
Externí odkaz:
http://arxiv.org/abs/2312.09921
Publikováno v:
EEE Access, vol. 8, 2020
There is a consensus about the good sensing characteristics of Twitter to mine and uncover knowledge in financial markets, being considered a relevant feeder for taking decisions about buying or holding stock shares and even for detecting stock manip
Externí odkaz:
http://arxiv.org/abs/2312.11531
Publikováno v:
Multimed Tools Appl 78, 2019
There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their
Externí odkaz:
http://arxiv.org/abs/2312.11530