A privacy-preserving federated learning approach for airline upgrade optimization

Sien Chen and Yinghua Huang

Journal of Air Transport Management, 2025, vol. 122, issue C

Abstract: A key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos†between themselves and other business partners for a more holistic view of customers' consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers.

Keywords: Federated learning; Data silos; Privacy preservation; Confidential multi-party computation; Upgrade optimization; Airlines (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:122:y:2025:i:c:s0969699724001583

DOI: 10.1016/j.jairtraman.2024.102693

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