: Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE: a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources. For complete details on the use and execution of this protocol, please refer to Casella et al.1.

Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis

Casella, Bruno
;
Aldinucci, Marco;
2024-01-01

Abstract

: Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE: a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources. For complete details on the use and execution of this protocol, please refer to Casella et al.1.
2024
5
1
1
31
https://star-protocols.cell.com/protocols/3225
https://www.sciencedirect.com/science/article/pii/S2666166723007797?via=ihub
Bioinformatics; Clinical Protocol; Computer sciences; Health Sciences
Casella, Bruno; Riviera, Walter; Aldinucci, Marco; Menegaz, Gloria
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1950975
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