Breaking Data Silos: Towards Open and Scalable Mobility Foundation
  Models via Generative Continual Learning
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.
684ba6bc3b733ba333687093
Yuan Yuan
684ba6bc3b733ba333687094
Yukun Liu
684ba6bc3b733ba333687095
Chonghua Han
684ba6bc3b733ba333687096
Jie Feng
claimed_verified
2025-06-13T07:39:13.023Z
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/avatars/b2798bd5f8368f956bf7fab79d9432f0.svg
Jie Feng
user
JJ-TMT
684ba6bc3b733ba333687097
Yong Li
Breaking Data Silos: Towards Open and Scalable Mobility Foundation
  Models via Generative Continual Learning
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.
MoveGCL is a privacy-preserving framework using generative continual learning and a Mixture-of-Experts Transformer for training mobility foundation models without sharing raw data.
generative continual learning privacy-preserving Mixture-of-Experts Transformer mobility-aware expert routing mechanism layer-wise progressive adaptation catastrophic forgetting federated learning