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Vertical federated learning for transport mode detection using multi-modality data
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DOI：10.1016/j.trc.2026.105546.png)
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Abstract
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• A semi-supervised vertical federated learning framework for transport mode detection is introduced. • The framework integrates multi-modality features without sharing raw IMU, GPS, or cellular data. • The proposed representation alignment and knowledge distillation enhance weak-modality learning. • The proposed framework supports reliable single-modality inference under missing or sparse data conditions. • Experiments on the SHL dataset show substantial gains over state-of-the-art baselines.
Keywords:
Transport mode detection
Multi-modality
Vertical federated learning
Semi-supervised learning
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