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

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

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...

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Bibliographic Details
Main Authors: Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Tourism and Hospitality
Subjects:
travel recommendation
federated learning
personalized travel itinerary generation
privacy preserving
phase-adaptive federated learning
Online Access:https://www.mdpi.com/2673-5768/6/2/100
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