跨城市联邦迁移学习框架--以城市区域剖面为例
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
摘要：
数据不足问题（即，由于服务和基础设施不足或城市发展水平不平衡而导致的数据缺失和标签稀缺）严重影响了真实场景中的城市计算任务。现有的迁移学习方法启发了对数据不足的优雅解决方案，但仅关注一类不足问题，而未能兼顾两方面。此外，大多数以前的跨城市传输方法忽略了城市间的数据隐私，这是在实际应用中公众关注的问题。为了解决上述挑战性问题，我们提出了一个新的跨城市联邦迁移学习框架（CcFTL），以应对数据不足和隐私问题。具体而言，CcFTL将关系知识从多个数据丰富的源城市转移到目标城市。此外，特定于目标任务的模型参数首先在源数据上训练，然后通过参数传递微调到目标城市。通过采用联邦训练和同态加密设置，CcFTL能够有效地解决城市间的数据隐私问题。我们将城市区域分析作为智慧城市的一个应用，并通过实际研究评估所提出的方法。实验表明，我们的框架比几个有竞争力的国家的最先进的方法显着的优越性。
英文摘要：
Data insufficiency problems (i.e., data missing and label scarcity) caused by inadequate services and infrastructures or imbalanced development levels of cities have seriously affected the urban computing tasks in real scenarios. Prior transfer learning methods inspire an elegant solution to the data insufficiency, but are only concerned with one kind of insufficiency issue and fail to give consideration to both sides. In addition, most previous cross-city transfer methods overlook inter-city data privacy which is a public concern in practical applications. To address the above challenging problems, we propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems. Concretely, CcFTL transfers the relational knowledge from multiple rich-data source cities to the target city. Besides, the model parameters specific to the target task are firstly trained on the source data and then fine-tuned to the target city by parameter transfer. With our adaptation of federated training and homomorphic encryption settings, CcFTL can effectively deal with the data privacy problem among cities. We take the urban region profiling as an application of smart cities and evaluate the proposed method with a real-world study. The experiments demonstrate the notable superiority of our framework over several competitive state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
