Listar1. Investigación por tema "Federated learning"
Mostrando ítems 1-8 de 8
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Concept Drift Detection and Adaptation for Federated and Continual Learning
(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 ... -
Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging
(Institute of Electrical and Electronics Engineers, 2023)[Abstract]: Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, while promising results have reported in past literature, uptake ... -
Ensemble and continual federated learning for classification tasks
(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 ... -
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 ... -
Fed-mRMR: A lossless federated feature selection method
(Elsevier, 2024-05)[Abstract]: Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, ... -
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, ... -
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead
(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 ... -
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 ...
