Deep-Attention Model to Analyze Reliable Customers via Federated Learning
Peer reviewed, Journal article
Published version
Åpne
Permanent lenke
https://hdl.handle.net/11250/2992593Utgivelsesdato
2021Metadata
Vis full innførselSamlinger
Originalversjon
Ahmed, U., Lin, J. C.-W., & Srivastava, G. (2021). Deep-attention model to analyze reliable customers via federated learning. In 2021 International Joint Conference on Neural Networks (IJCNN). 10.1109/IJCNN52387.2021.9533486Sammendrag
In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model is used with a federated learning framework. The edge devices compute the model updates using local data and send them to the server for aggregation. Repetition is performed in multiple rounds until a convergence point is reached. The transaction data is used to train the attention model that gives a low dimensional embedding. Afterward, we share the convergence model among the client/stores. Then, efficient clustering-based dynamic method is then utilized. For experimentation, we used retail store data to cluster the customer based on purchase behavior. The proposed clustering method used semantic embedding to extract centroid and then cluster them by discovering relevant patterns. The method achieved the 0.75 ROC values for the random distribution and 0.70 for the fixed distribution. The clustering method can help to reduce communication costs while ensuring privacy.
Beskrivelse
Will not be made available, for copyright reasons