Upgrade Optimization in the Airline Industry_A Privacy Preserving Federated Learning Approach

Sien Chen, Yinghua Huang, Wei Jiang, Jueying Zhang, Dong-Ling Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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. Using a case study of an airline company, this study demonstrated how FL-based upgrade algorithms using multi-source data can be developed to improve upgrade prediction accuracy while preserving customers’ personal data privacy. This study offers significant theoretical and practical implications for upgrade optimization in the contexts of airlines and other hospitality-related businesses.
Original languageEnglish
Title of host publication2022 AMA Summer Academic Conference
Place of PublicationChicago, USA
PublisherAmerican Marketing Association (AMA)
Number of pages14
Publication statusUnpublished - Aug 2022


  • Marketing Analytics
  • AI
  • Machine Learning


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