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: 5.1453267e-05
Overall Rank: 9,654 | 35.10%
DOI: 10.1145/3588961

Incoming Non-self Citations Over Time

Authors

1. Rui Fu (Beijing Institute of Technology)
2. Yuncheng Wu (National University of Singapore)
3. Quanqing Xu (Ant Financial)
4. Meihui Zhang (Beijing Institute of Technology)

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
6,554	Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System	2023	VLDB	5.7499619e-05
8,541	Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	2024	VLDB	5.3192847e-05
11,324	PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning	2025	VLDB	4.9793485e-05

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
1,277	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00011237888
2,007	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	9.198944e-05
2,812	OceanBase: A 707 Million tpmC Distributed Relational Database System	2022	VLDB	7.9811649e-05
3,311	BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	2022	SIGMOD	7.4392547e-05
3,745	Automated Feature Engineering for Algorithmic Fairness	2021	VLDB	7.055875e-05
4,182	Rafiki: Machine Learning as an Analytics Service System	2019	VLDB	6.755131e-05
4,489	ARM-Net: Adaptive Relation Modeling Network for Structured Data	2021	SIGMOD	6.5781098e-05
4,541	Projected Federated Averaging with Heterogeneous Differential Privacy	2022	VLDB	6.5497191e-05
4,699	F-IVM: Learning over Fast-Evolving Relational Data	2020	SIGMOD	6.4633894e-05
5,037	Privacy Preserving Schema and Data Matching	2007	SIGMOD	6.3032648e-05
5,247	Enabling SQL-based Training Data Debugging for Federated Learning	2022	VLDB	6.2121767e-05
5,721	Federated Matrix Factorization with Privacy Guarantee	2022	VLDB	6.018334e-05
6,233	Fine-grained Concept Linking using Neural Networks in Healthcare	2018	SIGMOD	5.8420918e-05
6,771	Causal Feature Selection for Algorithmic Fairness	2022	SIGMOD	5.6864972e-05
7,839	ExDRa: Exploratory Data Science on Federated Raw Data	2021	SIGMOD	5.4432099e-05
7,989	An Introduction to Federated Computation	2022	SIGMOD	5.4116268e-05
13,735	DyHealth: Making Neural Networks Dynamic for Effective Healthcare Analytics	2022	VLDB	-

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