Human mobility modeling is a complex yet essential subject of study related to modeling important spatiotemporal events, including traffic, disease spreading, and customized directions and recommendations. While spatiotemporal data can be collected easily via smartphones, current state-of-the-art deep learning methods require vast amounts of such privacy-sensitive data to generate useful models. This work investigates the creation of spatiotemporal models using a Federated Learning (FL) approach—a machine learning technique that avoids sharing personal data with centralized servers. More specifically, we examine three centralized models for next-place prediction: a simple Gated Recurrent Unit (GRU) model, as well as two state-of-the-art centralized approaches, Flashback and DeepMove. Flashback is a Recurrent Neural Network (RNN) that utilizes historical hidden states with similar context as the current spatiotemporal context to improve performance. DeepMove is an attentional RNN that aims to capture human mobility's regularity while coping with data sparsity. We then implemented models based on FL for the two best-performing centralized models. We compared the performance of all models using two large public datasets: Foursquare (9,450 million check-ins, February 2009 to October 2010) and Gowalla (3,300 million check-ins, April 2012 to January 2014). We first replicated the performance of both Flashback and DeepMove, as reported in the original studies, and compared them to the simple GRU model. Flashback and GRU proved to be the best performing centralized models, so we further explored both in FL scenarios, including several parameters such as the number of clients, rounds, and epochs. Our results indicated that the training process of the federated models was less stable, i.e., the FL versions of both Flashback and GRU tended to have higher variability in the loss curves. The higher variability led to a slower convergence and thus a poorer performance when compared to the corresponding centralized models. Model performance was also highly influenced by the number of federated clients and the sparsity of the evaluation dataset. We additionally provide insights into the technical challenges of applying FL to state-of-the-art deep learning methods for human mobility.
Ezequiel, C. E. J., Gjoreski, M., & Langheinrich, M. (2022). Federated Learning for Privacy-Aware Human Mobility Modeling. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.867046
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