Abstract
In this paper, we first propose a federated learning-based embedding model for transaction classification. The model takes the transaction data as a set of frequent item-sets. After that model is able to learn low dimensional continuous vector by preserving the frequent item-sets contextual relationship. Results then indicated that the designed model can help and improve the decision boundary by reducing the global loss function.
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Ahmed, U., Lin, J.CW., Srivastava, G., Fournier-Viger, P. (2021). A Transaction Classification Model of Federated Learning. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_43
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