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A privacy-preserving federated learning approach for airline upgrade optimization

Author

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  • Chen, Sien
  • Huang, Yinghua

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.

Suggested Citation

  • Chen, Sien & Huang, Yinghua, 2025. "A privacy-preserving federated learning approach for airline upgrade optimization," Journal of Air Transport Management, Elsevier, vol. 122(C).
    • Handle: RePEc:eee:jaitra:v:122:y:2025:i:c:s0969699724001583
    DOI: 10.1016/j.jairtraman.2024.102693
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    References listed on IDEAS

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    1. Övünç Yılmaz & Pelin Pekgün & Mark Ferguson, 2017. "Would You Like to Upgrade to a Premium Room? Evaluating the Benefit of Offering Standby Upgrades," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 1-18, February.
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    3. Övünç Yılmaz & Pelin Pekgün & Mark Ferguson, 2017. "Would You Like to Upgrade to a Premium Room? Evaluating the Benefit of Offering Standby Upgrades," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 1-18, February.
    4. Andreas Krämer & Mark Friesen & Tom Shelton, 2018. "Are airline passengers ready for personalized dynamic pricing? A study of German consumers," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 115-120, April.
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    6. Steinhardt, Claudius & Gönsch, Jochen, 2012. "Integrated revenue management approaches for capacity control with planned upgrades," European Journal of Operational Research, Elsevier, vol. 223(2), pages 380-391.
    7. Haokun Fang & Quan Qian, 2021. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning," Future Internet, MDPI, vol. 13(4), pages 1-20, April.
    8. Michael D. Wittman & Peter P. Belobaba, 2017. "Personalization in airline revenue management – Heuristics for real-time adjustment of availability and fares," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(4), pages 376-396, August.
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