FedBridge: A Federated Query Engine over Embedding-Heterogeneous Vector Databases
Summary: FedBridge enables federated querying across autonomous vector databases with incompatible embedding spaces via lightweight alignment. Its silo-probing sampling strategies improve retrieval efficiency, while a unified interface and adapters support diverse models, formats, and systems. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Yuxiang Wang (Beihang University)
- 2. Ruixi Hu (Beihang University)
- 3. Ziyuan He (Beihang University)
- 4. Zhilin Liang (Beihang University)
- 5. Xinyu Zhao (Beihang University)
- 6. Yongxin Tong (Beihang University)
BibTeX Citation
@article{wang_vldb26,
title = {{FedBridge: A Federated Query Engine over Embedding-Heterogeneous Vector Databases}},
author = {Wang, Yuxiang and Hu, Ruixi and He, Ziyuan and Liang, Zhilin and Zhao, Xinyu and Tong, Yongxin},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4498--4501},
doi = {10.14778/3827998.3828049},
url = {https://doi.org/10.14778/3827998.3828049},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,589 | Scalable Multi-Query Optimization for Exploratory Queries over Federated Scientific Databases | 2008 | VLDB | 7.1867376e-05 |
| 6,465 | Integrating Vector Databases across Embedding Models | 2026 | SIGMOD | 5.775649e-05 |
| 7,646 | FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases | 2025 | VLDB | 5.4772833e-05 |
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