Data collaboration analysis trains an autoencoder anomaly detector across organizations in one communication round and outperforms FedAvg and FedProx on journal entry data under non-i.i.d. conditions.
X., Gutierrez-Portela, F., Moreno Hernandez, J
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Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach
Data collaboration analysis trains an autoencoder anomaly detector across organizations in one communication round and outperforms FedAvg and FedProx on journal entry data under non-i.i.d. conditions.