SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 18 Sep 2024.
Builds on
14 of 41 references.
References · 14
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
- 2019An Introduction to Variational Autoencoderscited by 1,000
- 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
- 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
- 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 77
- 2019Split Learning for collaborative deep learning in healthcarecited by 73
- 2020Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directionscited by 65
- 2021Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approachcited by 22
- 2022Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approachcited by 12
- 2019The Creation and Validation of Load Time Series for Synthetic Electric Power Systemscited by 8
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.