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Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis
Bruno Casella1, Walter Riviera2, Marco Aldinucci1
1Computer Science Department, University of Turin, 10149 Turin, Italy.
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.

