Federated Representation Learning With Data Heterogeneity for Human Mobility Prediction

The advancement of smart wearable devices and location-based smart services has enabled a new paradigm for smart human mobility prediction (HMP), which has a broad range of applications in smart healthcare and smart cities. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the HMP model without sharing the highly sensitive location data with others. However, in real-world scenarios, federated human mobility prediction suffers from data heterogeneity challenge, which includes two main aspects: heterogeneity mobility patterns, and data scarcity. In this paper, we propose an end-to-end federated representation learning framework for human mobility prediction, named FR-HMP, to overcome all the above obstacles. Specially, in order to enhance the representation abilities of data-scarcity clients, a two-phase learning process is proposed. The clustering module could cluster similar clients together on the parameter server to address the heterogeneous mobility patterns, and the representation learning module learns the enhanced representations of each client through the graph learning layer and graph convolution layer on the third-part server. Finally, extensive experiments are conducted using two diverse real-world HMP datasets to show the advantages of FR-HMP over state-of-the-art methods.