Decentralized Subgraph Learning for Spatial-Temporal Data Modeling.

Spatial-temporal data modeling has attracted attention due to the massive spatial-temporal data acquired by sensors, as well as its importance in the real world. Most existing methods require transferring a huge volume of data from different parties to a central server, which is impractical due to conflicts of benefit and privacy concerns. A party only possesses a part of the entire spatial-temporal data (i.e., a subgraph), and subgraphs are isolated among parties. Federated Learning (FL) is an emerging framework for training models without sharing data, but it still has a high vulnerability when the central server fails. Besides, naively fusing models in most FL may have a negative impact on performance because of insufficient spatial relations among subgraphs and discrepant spatial-temporal patterns among subgraphs. To this end, we propose a Decentralized Subgraph Learning framework for Spatial-Temporal data modeling, namely DeSL-ST, which can efficiently handle the distributed subgraphs without the need of the central server. Specifically, DeSL-ST uses a cross-subgraph spatial relation learning module to tackle the issue of missing spatial relations between subgraphs. Then, a sparse transfer structure learning module is proposed to produce better-personalized models that are beneficial for each subgraph. Experiments on two traffic forecasting tasks demonstrate that DeSL-ST achieves state-of-the-art performance with lower peer-to-peer communication cost.