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This paper introduces Sheaf-based Federated Representation Learning (SFRL), a framework that aligns heterogeneous local representations via learnable sheaf restriction maps and a quadratic gluing regularizer, without assuming a shared global latent space. A decentralized algorithm (Sheaf-FRL) with convergence guarantees is proposed and shown to outperform baselines in cooperative classification under data and model heterogeneity.
This paper proposes a temporal planning framework for dynamic route optimization and disruption management in heterogeneous multi-gauge railway systems. It formulates railway operations as a temporal planning problem using PDDL 2.1, generates conflict-free timestamped operational plans, and reduces reliance on manual decision-making, evaluated on benchmark problems with up to 1,000 track points and 120 trains.