A Transaction Classification Model of Federated Learning.

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.