ListarLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) por tema "Federated learning"
Mostrando ítems 1-3 de 3
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Fast deep autoencoder for federated learning
(Elsevier Ltd, 2023-11)[Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network ... -
FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks
(Elsevier B.V., 2023)[Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, ... -
Towards federated feature selection: Logarithmic division for resource-conscious methods
(Elsevier, 2024)[Abstract]: Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information. In this paper, we propose an efficient and green feature selection method ...