A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling

TL;DR

This paper introduces a federated transfer learning framework for urban region profiling that addresses data insufficiency and privacy concerns across cities, demonstrating superior performance in real-world experiments.

Contribution

The novel CcFTL framework effectively combines federated learning and transfer learning to handle data scarcity and privacy issues in cross-city urban profiling.

Findings

Outperforms state-of-the-art methods in urban region profiling

Successfully preserves data privacy through federated training and encryption

Effectively transfers knowledge from data-rich to data-scarce cities

Abstract

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.