Reliable customer analysis using federated learning and exploring deep-attention edge intelligence

Abstract

The Internet of Things (IoT) and smart cities are flourishing with distributed systems in mobile wireless networks. As a result, an enormous amount of data are being generated for devices at the network edge. This results in privacy concerns, sensor data management issues, and data utilization issues. In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model 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 reached. The transaction data used to train the attention model that gives a low dimensional embedding. Afterwards, we share the convergence model among the client/stores. Then, efficient pattern mining methods known as a clustering-based dynamic method (CBDM) are applied. For experimentation, we used retail store data to cluster the customer based on purchase behaviour. The proposed clustering method used semantic embedding to extract 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.