<ul><li>MoveGCL is a privacy-preserving framework for training mobility foundation models via generative continual learning without sharing raw data.</li><li>It enables decentralized and progressive model evolution by replaying synthetic trajectories generated from a frozen teacher model, while reinforcing knowledge retention through a tailored distillation strategy.</li><li>MoveGCL incorporates a Mixture-of-Experts Transformer with a mobility-aware expert routing mechanism and utilizes a layer-wise progressive adaptation strategy to stabilize continual updates.</li><li>Experiments on six real-world urban datasets show that MoveGCL achieves performance comparable to joint training, surpassing federated learning baselines, and providing strong privacy protection.</li></ul>

Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning

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