Compositional Generative Modeling from Decentralized Data
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Computer Science > Machine Learning
Title:Compositional Generative Modeling from Decentralized Data
Abstract:Learning the compositional nature of the physical world requires joint observation of interacting factors. However, because practical data is often decentralized, these factors are fragmented across isolated silos. Existing decentralized generative approaches focus only on modeling the union of siloed data, overlooking novel combinations implied by the collective whole. To bridge this gap, we introduce Decentralized Compositional Flow Matching (DCFM), a framework that enforces structural constraints across the global set of generative factors, without exchanging any raw data. DCFM enables novel combinations to emerge through peer interactions, even when no single data source can independently support the composition. Empirically, DCFM substantially outperforms federated learning and mixture-of-experts baselines across conditional image generation, robotic spatial planning, and medical attribute co-occurrence modeling.
| Comments: | ICML 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.10153 [cs.LG] |
| (or arXiv:2606.10153v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10153
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Mashrur Mahmud Morshed [view email][v1] Mon, 8 Jun 2026 20:32:35 UTC (3,691 KB)
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