A Cross-City Federated Transfer Learning Framework: A Case Study on
Urban Region Profiling
- URL: http://arxiv.org/abs/2206.00007v1
- Date: Tue, 31 May 2022 12:41:01 GMT
- Title: A Cross-City Federated Transfer Learning Framework: A Case Study on
Urban Region Profiling
- Authors: Gaode Chen, Yijun Su, Xinghua Zhang, Anmin Hu, Guochun Chen, Siyuan
Feng, Ji Xiang, Junbo Zhang, Yu Zheng
- Abstract summary: We propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems.
CcFTL transfers the relational knowledge from multiple rich-data source cities to the target city.
We take the urban region profiling as an application of smart cities and evaluate the proposed method with a real-world study.
- Score: 24.103961649276584
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data insufficiency problem (i.e., data missing and label scarcity issues)
caused by inadequate services and infrastructures or unbalanced development
levels of cities has 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 fully explore these two issues existing in the real world. In
addition, cross-city transfer in existing methods overlooks the inter-city data
privacy which is a public concern in practical application. 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 models.
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