Breaking Data Silos: Towards Open and Scalable Mobility Foundation
  Models via Generative Continual Learning
Authors: name: Yuan Yuan, name: Yukun Liu, name: Chonghua Han, name: Jie Feng, name: Yong Li

Foundation models have revolutionized fields such as natural language
processing and computer vision by enabling general-purpose learning across
diverse tasks and datasets. However, building analogous models for human
mobility remains challenging due to the privacy-sensitive nature of mobility
data and the resulting data silos across institutions. To bridge this gap, we
propose MoveGCL, a scalable and privacy-preserving framework for training
mobility foundation models via generative continual learning. Without sharing
raw data, MoveGCL enables decentralized and progressive model evolution by
replaying synthetic trajectories generated from a frozen teacher model, and
reinforces knowledge retention through a tailored distillation strategy that
mitigates catastrophic forgetting. To address the heterogeneity of mobility
patterns, MoveGCL incorporates a Mixture-of-Experts Transformer with a
mobility-aware expert routing mechanism, and employs a layer-wise progressive
adaptation strategy to stabilize continual updates. Experiments on six
real-world urban datasets demonstrate that MoveGCL achieves performance
comparable to joint training and significantly outperforms federated learning
baselines, while offering strong privacy protection. MoveGCL marks a crucial
step toward unlocking foundation models for mobility, offering a practical
blueprint for open, scalable, and privacy-preserving model development in the
era of foundation models.