Federated reinforcement learning for consumers privacy protection in Mobility-as-a-Service

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dc.contributor.authorChu, Kai-Fung
dc.contributor.authorGuo, Weisi
dc.date.accessioned2024-03-28T14:13:43Z
dc.date.available2024-03-28T14:13:43Z
dc.date.issued2024-02-13
dc.description.abstractMobility-as-a-Service (MaaS) offers multi-modal transport modes in a single service platform, which requires tremendous data and software support. Among various types of data, consumers' data is vulnerable to the communication channel as it must be transmitted from the consumer end to the MaaS. Consumers put a high priority on the privacy of their data in selecting a service. This motivates the need for a secure information management system for MaaS to protect consumers' information from leakage. In this paper, we propose a federated reinforcement learning (FRL) approach for the information exchange intensive multi-modal journey planning process. The FRL approach protects the information from malicious information thieves by federating the global model training to a local one without sensitive information exchange while maintaining the same solution quality of enhancing MaaS profit and consumer satisfaction. We perform experiments on a test case based on New York City data. The results demonstrate that the FRL approach is effective in the MaaS multi-modal journey planning process. Compared to the baseline approaches, consumer satisfaction and MaaS profit increase by about 12% and 74%, respectively. This pilot study not only provides privacy protection insight into the MaaS multi-modal journey planning but also other privacy-concern applications.en_UK
dc.description.sponsorshipThis work was supported by EPSRC MACRO - Mobility as a service: Managing Cybersecurity Risks across Consumers, Organisations and Sectors (EP/V039164/1).en_UK
dc.identifier.citationChu KF, Guo W. (2023) Federated reinforcement learning for consumers privacy protection in Mobility-as-a-Service. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 24-28 September 2023, Bilbao, Spain, pp. 4840-4846en_UK
dc.identifier.eisbn979-8-3503-9946-2
dc.identifier.eissn2153-0017
dc.identifier.isbn979-8-3503-9947-9
dc.identifier.issn2153-0009
dc.identifier.urihttps://doi.org/10.1109/ITSC57777.2023.10422279
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21107
dc.language.isoen_UKen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMobility as a serviceen_UK
dc.subjectTrainingen_UK
dc.subjectPrivacyen_UK
dc.subjectReinforcement learningen_UK
dc.subjectComputer architectureen_UK
dc.subjectPlanningen_UK
dc.subjectInformation exchangeen_UK
dc.titleFederated reinforcement learning for consumers privacy protection in Mobility-as-a-Serviceen_UK
dc.typeConference paperen_UK

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