Listar por autor "Barro, Senén"
Mostrando ítems 1-5 de 5
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Concept Drift Detection and Adaptation for Federated and Continual Learning
Casado, Fernando E.; Lema, Dylan; Criado, Marcos F.; Iglesias, Roberto; Regueiro, Carlos V.; Barro, Senén (Springer, 2021)[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 ... -
Ensemble and continual federated learning for classification tasks
Casado, Fernando E.; Lema, Dylan; Iglesias, Roberto; Regueiro, Carlos V.; Barro, Senén (Springer, 2023-09)[Abstract]: 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 ... -
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead
Criado, Marcos F.; Casado, Fernando E.; Iglesias Rodríguez, Roberto; Regueiro, Carlos V.; Barro, Senén (Elsevier, 2022)[Abstract] Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone ... -
Telemonitorización de pacientes: una experiencia en las unidades de cuidados coronarios
Presedo, Jesús; Vila, J.; Castro, Daniel; Fernández-Delgado, M.; Fraga, S.; Lama, M.; Barro, Senén (Universidade da Coruña, 2001) -
Walking Recognition in Mobile Devices
Casado, Fernando E.; Rodríguez García, Germán; Iglesias Rodríguez, Roberto; Regueiro, Carlos V.; Barro, Senén; Canedo-Rodriguez, Adrián (MDPI AG, 2020-02-21)[Abstract] Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for ...