O2O Logistics Customer Value Prediction with Periodic Asynchronous Vertical Federated Learning

Recent years have witnessed significant advancements in O2O logistics, which require predicting the volume of shipments generated by customers, commonly referred to as customer value. The essence of accurately predicting customer value in O2O logistics involves analyzing both online buying habits and offline logistics operations. Existing customer value prediction efforts focus solely on online or offline features, making them unsuitable for O2O scenarios. In this paper, we investigate the integration of both online and offline features for customer value prediction, which faces challenges including (i) data silos issues between logistics platforms and e-commerce platforms, and (ii) feature heterogeneity between online and offline data. To address these challenges, we propose a Periodic Asynchronous Vertical Federated Learning framework with adaptive Feature Selection (PAVFL-FS), enabling logistics platforms to efficiently and accurately predict customer value in collaboration with the e-commerce platforms. PAVFL-FS consists of two components: (i) a periodic asynchronous vertical federated learning algorithm to handle data silos problem and enable efficient model training; (ii) a local feature selection algorithm based on stochastic gates to address the cross-platform feature heterogeneity. We have conducted experimental validation on a large-scale real-world dataset collected from a major logistics company in China, including over 2.4 million waybill records and over 6 million online sales records from more than 3,500 merchants. The experimental results demonstrate that PAVFL-FS achieves a mean absolute error of 0.52 in O2O logistics customer value prediction, outperforming 24.6% to the baseline.