Learning to Select Instance: Simultaneous Transfer Learning and Clustering

Huan, Zhaoxin; Wang, Yulong; He, Yong; Zhang, Xiaolu; Fu, Chilin; Wu, Weichang; Zhou, Jun; Ding, Ke; Zhang, Liang; Mo, Linjian

doi:10.1145/3404835.3462992

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…To this end, there are mainly four kinds of approaches: 1) instance-based transfer [27], [28], which assumes that certain parts of the data in the source domain can be reused for learning in the target domain by re-weighting; 2) featurerepresentation transfer [29]- [31]. The intuitive idea behind this case is to learn a "good" feature representation for the target domain; 3) parameter transfer [32]- [34], which assumes that the source tasks and the target tasks share some parameters or prior distributions of the hyper-parameters of the models.…”

Section: Transfer Learningmentioning

confidence: 99%

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

Chen¹,

Su²,

Zhang³

et al. 2022

Preprint

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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 give consideration to both sides. In addition, most previous cross-city transfer methods overlooks the inter-city data privacy which is a public concern in practical application. To address 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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