Phase-Adaptive Federated Learning for Privacy-Preserving Personalized Travel Itinerary Generation

Xiaolong Chen, Hongfeng Zhang () and Cora Un In Wong ()
Additional contact information
Xiaolong Chen: Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China
Hongfeng Zhang: Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China
Cora Un In Wong: Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China

Tourism and Hospitality, 2025, vol. 6, issue 2, 1-19

Abstract: We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 ( p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization.

Keywords: travel recommendation; federated learning; personalized travel itinerary generation; privacy preserving; phase-adaptive federated learning (search for similar items in EconPapers)
JEL-codes: Z3 Z30 Z31 Z32 Z33 Z38 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2673-5768/6/2/100/pdf (application/pdf)
https://www.mdpi.com/2673-5768/6/2/100/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jtourh:v:6:y:2025:i:2:p:100-:d:1670444

Access Statistics for this article

Tourism and Hospitality is currently edited by Mr. Philip Li

More articles in Tourism and Hospitality from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().