<p>Federated learning: General review and future directions</p>

María Chavero-Díez; Socayna Jouide; Carles Hernandez-Ferrer; Laia Codó; Karim Lekadir; Salvador Capella-Gutierrez; Josep Lluis Gelpí

doi:10.7490/f1000research.1119436.1

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Chavero-Díez M, Jouide S, Hernandez-Ferrer C et al. Federated learning: General review and future directions [version 1; not peer reviewed]. F1000Research 2023, 12(ELIXIR):578 (poster) (https://doi.org/10.7490/f1000research.1119436.1)

NOTE: it is important to ensure the information in square brackets after the title is included in this citation.

Federated learning: General review and future directions

María Chavero-Díez¹, Socayna Jouide, Carles Hernandez-Ferrer, Laia Codó, Karim Lekadir, Salvador Capella-Gutierrez, Josep Lluis Gelpí

Published 31 May 2023 (https://doi.org/10.7490/f1000research.1119436.1)

Author Affiliations

¹ Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain

Copyright © 2023 Chavero-Díez M et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 31 May 2023

Federated learning: General review and future directions
[version 1; not peer reviewed]

María Chavero-Díez¹, Socayna Jouide, Carles Hernandez-Ferrer, Laia Codó, Karim Lekadir, Salvador Capella-Gutierrez, Josep Lluis Gelpí

Author Affiliations

¹ Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain

Presented at

ELIXIR All Hands 2023

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Federated learning, Federated processing system, Differential privacy, Sensitive data, Review, Flower

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