Logistics data sharing method based on federated learning

ZhiHui Wang; DeQian Fu; Jiawei Zhang

doi:10.1117/12.2667310

28 March 2023 Logistics data sharing method based on federated learning

ZhiHui Wang, DeQian Fu, Jiawei Zhang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125661H (2023) https://doi.org/10.1117/12.2667310
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

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.

Citation Download Citation

ZhiHui Wang, DeQian Fu, and Jiawei Zhang "Logistics data sharing method based on federated learning", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125661H (28 March 2023); https://doi.org/10.1117/12.2667310

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