FedSQ: A Secure System for Federated Vector Similarity Queries
Summary: FedSQ performs privacy-preserving federated vector similarity queries across multiple data owners via secure multi-party computation. Uniquely combines MPC with indexing and sampling optimizations to trade off efficiency and accuracy for high-dimensional embeddings. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zeqi Zhu (Beihang University)
- 2. Zeheng Fan (Beihang University)
- 3. Yuxiang Zeng (Beihang University)
- 4. Yexuan Shi (Beihang University)
- 5. Yi Xu (Beihang University)
- 6. Mengmeng Zhou (Beijing Academy of Artificial Intelligence)
- 7. Jin Dong (Beijing Academy of Artificial Intelligence)
BibTeX Citation
@article{zhu_vldb24,
title = {{FedSQ: A Secure System for Federated Vector Similarity Queries}},
author = {Zhu, Zeqi and Fan, Zeheng and Zeng, Yuxiang and Shi, Yexuan and Xu, Yi and Zhou, Mengmeng and Dong, Jin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4441--4444},
doi = {10.14778/3685800.3685895},
url = {https://doi.org/10.14778/3685800.3685895},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,836 | Filtered Vector Search: State-of-the-art and Research Opportunities | 2025 | VLDB | 5.6658357e-05 |
| 7,652 | FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases | 2025 | VLDB | 5.4746904e-05 |
| 11,321 | Sectric: Towards Accurate, Privacy-preserving and Efficient Triangle Counting | 2025 | VLDB | 4.9769913e-05 |
| 11,353 | Federated and Balanced Clustering for High-dimensional Data | 2025 | VLDB | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 189 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.0002585319 |
| 3,307 | FedKNN: Secure Federated k-Nearest Neighbor Search | 2024 | SIGMOD | 7.4387525e-05 |
| 4,885 | Hu-Fu: Efficient and Secure Spatial Queries over Data Federation | 2022 | VLDB | 6.3680345e-05 |
| 9,390 | Hu-Fu: A Data Federation System for Secure Spatial Queries | 2022 | VLDB | 5.1843659e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,086 | New Trends in High-D Vector Similarity Search: AI-driven, Progressive, and Distributed | 2021 | VLDB |
| 2 | 6,467 | Integrating Vector Databases across Embedding Models | 2026 | SIGMOD |
| 3 | 8,416 | Fast Approximate Similarity Join in Vector Databases | 2025 | SIGMOD |
| 4 | 5,251 | Enabling SQL-based Training Data Debugging for Federated Learning | 2022 | VLDB |
| 5 | 11,037 | Advances of Query Processing in Vector Databases | 2026 | VLDB |
| 6 | 10,826 | FedAugment: Table Augmentation Search over Decentralized Data Repositories | 2026 | VLDB |
| 7 | 1,496 | SMCQL: Secure Querying for Federated Databases | 2017 | VLDB |
| 8 | 3,307 | FedKNN: Secure Federated k-Nearest Neighbor Search | 2024 | SIGMOD |
| 9 | 10,961 | FedBridge: A Federated Query Engine over Embedding-Heterogeneous Vector Databases | 2026 | VLDB |
| 10 | 7,652 | FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases | 2025 | VLDB |