Détail du document
Identifiant
oai:arXiv.org:2407.17228
Sujet
Statistics - Machine Learning Computer Science - Machine Learnin...Auteur
Damiani, Celeste Rodina, Yulia Decherchi, SergioCatégorie
Computer Science
Année
2024
Date de référencement
31/07/2024
Mots clés
features machine data learningRésumé
Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings.
Often, the data involved is not limited to clinical data but also includes additional omics features (e.g. proteomics).
Consequently, data is distributed not only across hospitals but also across omics centers, which are labs capable of generating such additional features from biosamples.
This scenario leads to a hybrid setting where data is scattered both in terms of samples and features.
In this hybrid setting, we present an efficient reformulation of the Kernel Regularized Least Squares algorithm, introduce two variants and validate them using well-established datasets.
Lastly, we discuss security measures to defend against possible attacks.
Damiani, Celeste,Rodina, Yulia,Decherchi, Sergio, 2024, A Hybrid Federated Kernel Regularized Least Squares Algorithm
MELAS: Phenotype Classification into Classic-versus-Atypical Presentations
presentations mitochondrial strokelike patients variability phenotype clinical melas
