Ensemble and continual federated learning for classification tasks

Autor: Fernando E. Casado, Dylan Lema, Roberto Iglesias, Carlos V. Regueiro, Senén Barro

Publikováno v: Machine Learning.

Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bd4e8076972a4d42447cd742e3d941c9
https://doi.org/10.1007/s10994-023-06330-z

Mobile Robot Positioning with 433-MHz Wireless Motes with Varying Transmission Powers and a Particle Filter

Autor: Adrian Canedo-Rodriguez, Jose Manuel Rodriguez, Victor Alvarez-Santos, Roberto Iglesias, Carlos V. Regueiro

Publikováno v: Sensors, Vol 15, Iss 5, Pp 10194-10220 (2015)

In wireless positioning systems, the transmitter’s power is usually fixed. In this paper, we explore the use of varying transmission powers to increase the performance of a wireless localization system. To this extent, we have designed a robot posi

Externí odkaz: https://doaj.org/article/ce90119a688044799a2ec20ef25507fb

Robust Step Counting for Inertial Navigation with Mobile Phones

Autor: Germán Rodríguez, Fernando E. Casado, Roberto Iglesias, Carlos V. Regueiro, Adrián Nieto

Publikováno v: Sensors, Vol 18, Iss 9, p 3157 (2018)

Mobile phones are increasingly used for purposes that have nothing to do with phone calls or simple data transfers, and one such use is indoor inertial navigation. Nevertheless, the development of a standalone application able to detect the displacem

Externí odkaz: https://doaj.org/article/e9c655396928402a9d60402775cb5e5e

Dataset bias exposed in face verification

Autor: Carlos V. Regueiro, Roberto Iglesias, Xosé M. Pardo, Eric Lopez-Lopez, Fernando E. Casado

Publikováno v: Minerva: Repositorio Institucional de la Universidad de Santiago de Compostela
Universidad de Santiago de Compostela (USC)
Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname

This is the peer reviewed version of the following article: López‐López, E., Pardo, X.M., Regueiro, C.V., Iglesias, R. and Casado, F.E. (2019), Dataset bias exposed in face verification. IET Biom., 8: 249-258, which has been published in final fo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faa8e57dbd60b177f5f4d7af6ea211b4
https://doi.org/10.1049/iet-bmt.2018.5224

Concept drift detection and adaptation for federated and continual learning

Autor: Fernando E. Casado, Marcos F. Criado, Roberto Iglesias, Senén Barro, Dylan Lema, Carlos V. Regueiro

Publikováno v: Minerva: Repositorio Institucional de la Universidad de Santiago de Compostela
Universidad de Santiago de Compostela (USC)
RUC. Repositorio da Universidade da Coruña
instname
Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela

[Abstract] Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c4600b192eb0be774eea2e1e4e94108
https://hdl.handle.net/10347/26638