  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.
