Logistics data sharing method based on federated learning

Abstract

In today's era of big data, the logistics supply chain generates massive amounts of data at all stages, and the privacy issues of logistics data are increasingly prominent. In order to efficiently utilize the logistics data of each enterprise to meet the needs of the enterprise and achieve secure data sharing, a federated learning-based logistics data sharing scheme is proposed. Using federated learning to federate multiple sources of data for modelling, the reputation value of each enterprise is stored on the blockchain and the enterprises that provide high quality data sharing are rewarded. Finally, the effectiveness of the scheme and the impact of data quality and algorithm selection on model training are verified through simulation experiments.