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FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data

Summary: FEAST uses conditional mutual information for federated vertical feature selection on relational data, cutting redundancy. A compact, efficient protocol minimizes exchanged statistics to protect raw data, delivering strong accuracy at low cost. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h2b0187428ffac4f3
Venue
SIGMOD
Year
2023
Pagerank
5.142891e-05
Overall Rank
9,661 | 35.07%
DOI
10.1145/3588961

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fu_sigmod23,
        title = {{FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data}},
        author = {Fu, Rui and Wu, Yuncheng and Xu, Quanqing and Zhang, Meihui},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588961},
        url = {https://dl.acm.org/doi/10.1145/3588961},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,556 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.7472399e-05
8,548 Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs 2024 VLDB 5.3167667e-05
11,332 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,278 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00011232568
2,010 VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning 2021 SIGMOD 9.1945893e-05
2,813 OceanBase: A 707 Million tpmC Distributed Relational Database System 2022 VLDB 7.9773891e-05
3,312 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.4357331e-05
3,746 Automated Feature Engineering for Algorithmic Fairness 2021 VLDB 7.0525348e-05
4,182 Rafiki: Machine Learning as an Analytics Service System 2019 VLDB 6.7519862e-05
4,492 ARM-Net: Adaptive Relation Modeling Network for Structured Data 2021 SIGMOD 6.5749958e-05
4,542 Projected Federated Averaging with Heterogeneous Differential Privacy 2022 VLDB 6.5466185e-05
4,702 F-IVM: Learning over Fast-Evolving Relational Data 2020 SIGMOD 6.46033e-05
5,039 Privacy Preserving Schema and Data Matching 2007 SIGMOD 6.3002809e-05
5,251 Enabling SQL-based Training Data Debugging for Federated Learning 2022 VLDB 6.2092359e-05
5,722 Federated Matrix Factorization with Privacy Guarantee 2022 VLDB 6.015485e-05
6,236 Fine-grained Concept Linking using Neural Networks in Healthcare 2018 SIGMOD 5.83935e-05
6,775 Causal Feature Selection for Algorithmic Fairness 2022 SIGMOD 5.6838053e-05
7,843 ExDRa: Exploratory Data Science on Federated Raw Data 2021 SIGMOD 5.4406331e-05
7,994 An Introduction to Federated Computation 2022 SIGMOD 5.409065e-05
13,740 DyHealth: Making Neural Networks Dynamic for Effective Healthcare Analytics 2022 VLDB -
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