45 lines
6.6 KiB
Markdown
45 lines
6.6 KiB
Markdown
## Federated Learning for Tree-based Models
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This list is constantly being updated. Feel free to contribute!
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### 2022
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| Title | Venue | Link | key words |
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|-------------------------------------------------------------------------------------------------|----------|-----------------------------------------------------------------------------------------------------------------------|------------------|
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| Vertical Federated Learning | arxiv | [pdf](https://arxiv.org/pdf/2211.12814.pdf) | survey |
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| Tree-Based Models for Federated Learning Systems | Springer | [pdf](https://link.springer.com/chapter/10.1007/978-3-030-96896-0_2) | survey |
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| Practical Privacy Attacks on Vertical Federated Learning | arxiv | [pdf](https://arxiv.org/pdf/2011.09290.pdf) | vertical, attack |
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| OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | VLDB | [pdf](https://arxiv.org/pdf/2210.01318.pdf), [code](https://github.com/alibaba-edu/mpc4j/tree/main/mpc4j-sml-opboost) | vertical |
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| Federated Boosted Decision Trees with Differential Privacy | arxiv | [pdf](https://arxiv.org/pdf/2210.02910.pdf) | horizontal |
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| An Efficient and Robust System for Vertically Federated Random Forest | arxiv | [pdf](https://arxiv.org/pdf/2201.10761.pdf) | vertical |
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| FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging | arxiv | [pdf](https://arxiv.org/pdf/2204.00976.pdf) | vertical |
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### 2021
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| Title | Venue | Link | key words |
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| ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | --------- |
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| Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical Federated Learning | IEEE International Conference on Big Data | [pdf](https://arxiv.org/pdf/2105.09540.pdf) | vertical |
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| Large-Scale Secure XGB for Vertical Federated Learning | CIKM | [pdf](https://arxiv.org/pdf/2005.08479.pdf), [code](https://github.com/secretflow/secretflow) | vertical |
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| SecureBoost: A Lossless Federated Learning Framework | IEEE Intelligent Systems | [pdf](https://arxiv.org/pdf/1901.08755.pdf), [code](https://github.com/FederatedAI/FATE) | vertical |
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| An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization | ACM Transactions on Intelligent Systems and Technology | [pdf](https://arxiv.org/pdf/2105.05717.pdf) | vertical |
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| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | SIGMOD | [pdf](https://dl.acm.org/doi/abs/10.1145/3448016.3457241) | vertical |
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| SoK: Privacy-Preserving Collaborative Tree-based Model Learning | PoPETs | [pdf](https://arxiv.org/pdf/2103.08987.pdf) | Survey |
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### 2020
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| Title | Venue | Link | key words |
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|------------------------------------------------------------------------|-------|-----------------------------------------------------|----------------------|
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| Practical federated gradient boosting decision trees | AAAI | [pdf](https://arxiv.org/pdf/1911.04206.pdf) | horizontal |
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| FederBoost: Private federated learning for GBDT | arxiv | [pdf](https://arxiv.org/pdf/2011.02796.pdf) | vertical, horizontal |
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| Privacy Preserving Vertical Federated Learning for Tree-based Models | VLDB | [pdf](http://www.vldb.org/pvldb/vol13/p2090-wu.pdf) | vertical |
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| Privacy-Preserving Gradient Boosting Decision Trees | AAAI | [pdf](https://arxiv.org/pdf/1911.04209.pdf) | differential privacy |
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| Adaptive histogram-based gradient boosted trees for federated learning | arxiv | [pdf](https://arxiv.org/pdf/2012.06670.pdf) | horizontal |
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### 2019
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| Title | Venue | Link | key words |
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|----------------------------------------------------------------------------------------|-------------------------------------------|---------------------------------------------|------------|
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| SecureGBM: Secure Multi-Party Gradient Boosting | IEEE International Conference on Big Data | [pdf](https://arxiv.org/pdf/1911.11997.pdf) | vertical |
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| Federated Forest | IEEE Transactions on Big Data | [pdf](https://arxiv.org/pdf/1905.10053.pdf) | vertical |
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| The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost | arxiv | [pdf](https://arxiv.org/pdf/1907.07157.pdf) | horizontal |
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### 2018
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| Title | Venue | Link | key words |
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|------------------------------------------------------------------|---------------------------------|---------------------------------------------|------------|
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| Privacy-preserving collaborative prediction using random forests | AMIA Jt Summits Transl Sci Proc | [pdf](https://arxiv.org/pdf/1811.08695.pdf) | horizontal |
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