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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
6133
Venue
SIGMOD
Year
2021
Pagerank
0.00010171063
Overall Rank
1,899 | 86.81%
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
3,475 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0590045e-05
4,282 FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification 2022 VLDB 6.2824978e-05
5,521 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4671745e-05
6,455 Practical Differentially Private and Byzantine-resilient Federated Learning 2023 SIGMOD 5.0507487e-05
6,498 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.031352e-05
7,487 Incentive-Aware Decentralized Data Collaboration 2023 SIGMOD 4.7135369e-05
7,535 Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent 2023 VLDB 4.7131061e-05
8,453 Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs 2024 VLDB 4.5022073e-05
9,328 FEAST: A Communication-efficient Federated Feature Selection Framework for Relational Data 2023 SIGMOD 4.351469e-05
9,965 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 4.2229209e-05
10,402 SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost 2025 SIGMOD 4.1905499e-05
10,409 Sequoia: An Accessible and Extensible Framework for Privacy-Preserving Machine Learning over Distributed Data 2025 SIGMOD 4.1905499e-05
10,692 Federated Incomplete Tabular Data Prediction with Missing Complementarity 2025 VLDB 4.1905499e-05
10,694 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.1905499e-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 0.00044753012
1,099 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00014063542
2,636 PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce 2009 VLDB 8.401513e-05
2,954 Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries 2018 VLDB 7.8218804e-05
4,924 An Experimental Evaluation of Large Scale GBDT Systems 2019 VLDB 5.8211961e-05
9,471 DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions 2018 SIGMOD 4.3300949e-05
Previous Page 1 / 1 Next

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Overall Rank Paper Year Venue Pagerank
10,694 PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning 2025 VLDB 4.1905499e-05
6,498 Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System 2023 VLDB 5.031352e-05
9,965 Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates 2022 VLDB 4.2229209e-05
7,489 DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning 2023 SIGMOD 4.7135369e-05
6,615 Differentially Private Vertical Federated Clustering 2023 VLDB 4.9882647e-05
4,924 An Experimental Evaluation of Large Scale GBDT Systems 2019 VLDB 5.8211961e-05
3,475 BlindFL: Vertical Federated Machine Learning without Peeking into Your Data 2022 SIGMOD 7.0590045e-05
10,402 SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost 2025 SIGMOD 4.1905499e-05
5,521 OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization 2023 VLDB 5.4671745e-05
1,099 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00014063542