无需集中数据，用SplitVAE在隐私保护下生成高质量场景
SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
- 基于变分自编码器的分布式框架，不移动原始数据即可生成场景
- 在多主体网络中有效捕捉时空依赖，生成符合历史分布的场景
- 相比中心化方法更省传输成本，适合大规模隐私敏感场景
大规模多利益相关方网络系统（如电力系统和供应链）中的随机优化问题依赖数据驱动的场景来刻画复杂的时空依赖关系。然而，由于计算和物流瓶颈导致的数据孤岛，集中式数据聚合面临挑战。本文提出SplitVAEs，一种去中心化的场景生成框架，利用变分自编码器在不迁移利益相关方数据的前提下生成高质量场景。通过在分布式内存系统上的实验，我们展示了SplitVAEs在多个由大量利益相关方主导的领域中的广泛适用性。实验表明，SplitVAEs能有效学习大规模网络中的时空依赖关系，在去中心化条件下生成与各利益方历史联合分布一致的场景。与集中式先进基准方法相比，SplitVAEs在保持鲁棒性能的同时显著降低数据传输开销，提供了一种可扩展且保护隐私的场景生成替代方案。
原文摘要 · Abstract (English)
Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to the existence of data silos resulting from computational and logistical bottlenecks. In this paper, we present SplitVAEs, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of experiments on distributed memory systems, we demonstrate the broad applicability of SplitVAEs in a variety of domain areas that are dominated by a large number of stakeholders. 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 experiments show that SplitVAEs deliver robust performance compared to centralized, state-of-the-art benchmark methods while significantly reducing data transmission costs, leading to a scalable, privacy-enhancing alternative to scenario generation.
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