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MPT: Missing Prototype Tracking via Barycentric Reconstruction in Vehicular Federated Learning

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Cross-vehicle federated learning enables vehicles to collaboratively improve perception models while keeping locally collected driving data private. However, vehicle participation is transient, and a vehicle may depart before training converges while permanently taking its local data. When this departing vehicle holds most samples of a target class, the class becomes rare in the remaining FL network, and its recognition can silently degrade as the shared backbone continues to evolve. Recovering the class is difficult since the few remaining samples provide a noisy prototype estimate, while FL

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