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VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning

Summary: VF2Boost: a very fast vertical federated GBDT system for cross-enterprise learning. It combats idle waiting with a concurrent training protocol and speeds cryptography via custom operations, achieving 12.8–18.9x speedups and enabling larger datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6132
Venue
SIGMOD
Year
2021
Pagerank
0.00010180896
Overall Rank
1,895 | 86.82%
DOI
10.1145/3448016.3457241

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

Rank Citing Paper Year Venue Pagerank
3,506 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0291192e-05
4,290 FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification 2022 VLDB 6.2885419e-05
5,507 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4724291e-05
6,459 Practical Differentially Private and Byzantine-resilient Federated Learning 2023 SIGMOD 5.0556005e-05
6,502 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.0361846e-05
7,487 Incentive-Aware Decentralized Data Collaboration 2023 SIGMOD 4.7180617e-05
7,536 Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent 2023 VLDB 4.7176331e-05
8,459 Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs 2024 VLDB 4.5065275e-05
9,323 FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data 2023 SIGMOD 4.3556432e-05
9,966 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 4.2269436e-05
10,391 SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost 2025 SIGMOD 4.1945683e-05
10,398 Sequoia: An Accessible and Extensible Framework for Privacy-Preserving Machine Learning over Distributed Data 2025 SIGMOD 4.1945683e-05
10,684 Federated Incomplete Tabular Data Prediction with Missing Complementarity 2025 VLDB 4.1945683e-05
10,686 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
126 Space-Efficient Online Computation of Quantile Summaries 2001 SIGMOD 0.00044744986
1,143 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00013710269
2,630 PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce 2009 VLDB 8.4128091e-05
2,953 Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries 2018 VLDB 7.8267643e-05
4,975 An Experimental Evaluation of Large Scale GBDT Systems 2019 VLDB 5.79026e-05
9,469 DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions 2018 SIGMOD 4.3342363e-05
Previous Page 1 / 1 Next

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Overall Rank Paper Year Venue Pagerank
10,686 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.1945683e-05
6,502 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.0361846e-05
9,966 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 4.2269436e-05
7,489 DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning 2023 SIGMOD 4.7180617e-05
6,700 Differentially Private Vertical Federated Clustering 2023 VLDB 4.9563668e-05
4,975 An Experimental Evaluation of Large Scale GBDT Systems 2019 VLDB 5.79026e-05
3,506 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0291192e-05
10,391 SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost 2025 SIGMOD 4.1945683e-05
5,507 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4724291e-05
1,143 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00013710269