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Position Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 36

Federated Learning for Data Trust in Logistics

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DOI: http://dx.doi.org/10.15439/2023F5947

Citation: Position Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 36, pages 51–58 ()

Full text

Abstract. In the field of logistics, there is a significant shortage of qualified employees. Artificial Intelligence (AI) can help solve that problem supporting existing employees and reducing their workload. However, large amounts of data to train AI models are required and, in most cases, due to lack of trust between companies, model training is based solely on locally stored data from logistics providers and some publicly available datasets. To address this data scarcity issue, a proposed solution is to employ federated learning (FL), in the context of data trust (DT) by training AI models across multiple companies, based on both centralized data, within the DT platform and decentralized data from logistics providers data silos, while ensuring data sharing access at the attribute level. This paper proposes this approach and points out the importance of data sharing for effective model training for solving workforce challenges in logistics.

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