Federated Reinforcement Learning for Consumers Privacy Protection in Mobility-as-a-Service
Federated Reinforcement Learning for Consumers Privacy Protection in Mobility-as-a-Service
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DOI：
10.1109/itsc57777.2023.10422279
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Kai-Fung Chu;Weisi Guo
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文献类型：
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作者：
Kai-Fung Chu;Weisi Guo
移动即服务（MaaS）在单一服务平台中提供多式联运模式，这需要大量数据和软件支持。在各种类型的数据中，消费者的数据很容易受到通信通道的影响，因为它必须从消费者端传输到MaaS。消费者在选择服务时高度重视数据隐私。这促使 MaaS 需要一个安全的信息管理系统，以保护消费者的信息免遭泄露。在本文中，我们提出了一种用于信息交换密集型多模式旅程规划过程的联合强化学习（FRL）方法。 FRL 方法通过将全局模型训练联合到本地模型训练而无需敏感信息交换，从而保护信息免遭恶意信息窃贼的侵害，同时保持相同的解决方案质量，从而提高 MaaS 利润和消费者满意度。我们根据纽约市的数据对测试用例进行实验。结果表明，FRL 方法在 MaaS 多模式旅程规划过程中是有效的。与基准方法相比，消费者满意度和 MaaS 利润分别增加了约 12% 和 74%。这项试点研究不仅为 MaaS 多模式旅程规划提供了隐私保护见解，还为其他关注隐私的应用程序提供了见解。
Mobility-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.