Privacy Preserving Federated Graph Learning with Data Envelopment Analysis Driven Interpretable Customer Segmentation Framework

Customer segmentation plays a critical role in retail analytics by enabling personalized marketing, optimized resource allocation, and data-driven strategic decision-making. However, customer data is often distributed across multiple retail branches and contains sensitive transactional information, creating significant challenges related to privacy preservation, regulatory compliance, and model interpretability. Traditional segmentation approaches, including clustering algorithms and centralized deep learning methods, typically require aggregated data storage, which increases privacy risks and limits secure deployment in distributed retail environments. In addition, many existing methods fail to capture complex relational patterns such as co-purchasing behavior and inter-customer structural dependencies. To address these limitations, this study proposes FedGraph-DEA, a novel hybrid framework integrating federated learning, graph neural networks (GNNs), and Data Envelopment Analysis (DEA) for privacy-preserving and efficiency-aware customer segmentation. The framework first employs Distributed Federated Convolutional Autoencoders to extract latent customer representations from decentralized retail datasets. Similarity graphs are then constructed locally, followed by federated GNN-based community detection to identify structurally coherent customer groups without sharing raw data. Finally, DEA is applied to evaluate the operational efficiency of the discovered customer segments. Experimental evaluation was conducted using the UCI Online Retail dataset partitioned across five simulated non-IID client nodes. The proposed model achieved 96.1% accuracy, 0.95 precision, 0.96 recall, and 0.95 F1-score when compared with pseudo-ground-truth clusters generated through K-Means reference clustering. Furthermore, the framework obtained a silhouette score of 0.74 and a modularity value of 0.57, demonstrating strong cluster compactness and structural separation. The proposed system provides a scalable, interpretable, and privacy-preserving solution for distributed retail analytics.