## Federated Learning for Tree-based Models
This list is constantly being updated. Feel free to contribute!

### 2022
| Title                                                                                           | Venue    | Link                                                                                                                  | key words        |
|-------------------------------------------------------------------------------------------------|----------|-----------------------------------------------------------------------------------------------------------------------|------------------|
| Vertical Federated Learning                                                                     | arxiv    | [pdf](https://arxiv.org/pdf/2211.12814.pdf)                                                                           | survey           |
| Tree-Based Models for Federated Learning Systems                                                | Springer | [pdf](https://link.springer.com/chapter/10.1007/978-3-030-96896-0_2)                                                  | survey           |
| Practical Privacy Attacks on Vertical Federated Learning                                        | arxiv    | [pdf](https://arxiv.org/pdf/2011.09290.pdf)                                                                           | vertical, attack |
| 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         |
| Federated Boosted Decision Trees with Differential Privacy                                      | arxiv    | [pdf](https://arxiv.org/pdf/2210.02910.pdf)                                                                           | horizontal       | 
| An Efficient and Robust System for Vertically Federated Random Forest                           | arxiv    | [pdf](https://arxiv.org/pdf/2201.10761.pdf)                                                                           | vertical         |
| FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging    | arxiv    | [pdf](https://arxiv.org/pdf/2204.00976.pdf)                                                                           | vertical         |

### 2021
| Title                                                        | Venue                                                  | Link                                                         | key words |
| ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | --------- |
| 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  |
| Large-Scale Secure XGB for Vertical Federated Learning       | CIKM                                                   | [pdf](https://arxiv.org/pdf/2005.08479.pdf), [code](https://github.com/secretflow/secretflow) | vertical  |
| SecureBoost: A Lossless Federated Learning Framework         | IEEE Intelligent Systems                               | [pdf](https://arxiv.org/pdf/1901.08755.pdf), [code](https://github.com/FederatedAI/FATE) | vertical  |
| 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  |
| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | SIGMOD                                                 | [pdf](https://dl.acm.org/doi/abs/10.1145/3448016.3457241)    | vertical  |
| SoK: Privacy-Preserving Collaborative Tree-based Model Learning | PoPETs                                                 | [pdf](https://arxiv.org/pdf/2103.08987.pdf)                  | Survey    |

### 2020 
| Title                                                                  | Venue | Link                                                | key words            |
|------------------------------------------------------------------------|-------|-----------------------------------------------------|----------------------|
| Practical federated gradient boosting decision trees                   | AAAI  | [pdf](https://arxiv.org/pdf/1911.04206.pdf)         | horizontal           |
| FederBoost: Private federated learning for GBDT                        | arxiv | [pdf](https://arxiv.org/pdf/2011.02796.pdf)         | vertical, horizontal |
| Privacy Preserving Vertical Federated Learning for Tree-based Models   | VLDB  | [pdf](http://www.vldb.org/pvldb/vol13/p2090-wu.pdf) | vertical             |
| Privacy-Preserving Gradient Boosting Decision Trees                    | AAAI  | [pdf](https://arxiv.org/pdf/1911.04209.pdf)         | differential privacy |
| Adaptive histogram-based gradient boosted trees for federated learning | arxiv | [pdf](https://arxiv.org/pdf/2012.06670.pdf)         | horizontal           |

### 2019
| Title                                                                                  | Venue                                     | Link                                        | key words  |
|----------------------------------------------------------------------------------------|-------------------------------------------|---------------------------------------------|------------|
| SecureGBM: Secure Multi-Party Gradient Boosting                                        | IEEE International Conference on Big Data | [pdf](https://arxiv.org/pdf/1911.11997.pdf) | vertical   |
| Federated Forest                                                                       | IEEE Transactions on Big Data             | [pdf](https://arxiv.org/pdf/1905.10053.pdf) | vertical   |
| The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost | arxiv                                     | [pdf](https://arxiv.org/pdf/1907.07157.pdf) | horizontal |

### 2018
| Title                                                            | Venue                           | Link                                        | key words  |
|------------------------------------------------------------------|---------------------------------|---------------------------------------------|------------|
| Privacy-preserving collaborative prediction using random forests | AMIA Jt Summits Transl Sci Proc | [pdf](https://arxiv.org/pdf/1811.08695.pdf) | horizontal |