SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
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Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate uncertainties and complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to privacy, computational, and logistical bottlenecks. In this paper, we present SplitVAEs, a decentralized scenario generation framework that leverages the split learning paradigm and variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of large-scale, distributed memory-based experiments, we demonstrate the broad applicability of SplitVAEs in three distinct domain areas: power systems, industrial carbon emissions, and supply chains. Our experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Our results show that SplitVAEs outperform conventional state-of-the-art methodologies and provide a superior, computationally efficient, and privacy-compliant alternative to scenario generation.