# Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing

arXiv: https://arxiv.org/abs/1907.10218 · HTML: https://mlsanity.com/paper/1907.10218/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Federated Learning for Industrial Internet of Things in Future Industries](https://mlsanity.com/paper/2105.14659/) (2021)
- [An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization](https://mlsanity.com/paper/2105.05717/) (2021)
- [Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm](https://mlsanity.com/paper/2203.10329/) (2022)
- [When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System](https://mlsanity.com/paper/2102.10109/) (2021)
- [Federated Learning Versus Classical Machine Learning: A Convergence Comparison](https://mlsanity.com/paper/2107.10976/) (2021)
- [SoK: Privacy-Preserving Collaborative Tree-based Model Learning](https://mlsanity.com/paper/2103.08987/) (2021)
- [Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey](https://mlsanity.com/paper/2105.06300/) (2021)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Aggregation Delayed Federated Learning](https://mlsanity.com/paper/2108.07433/) (2021)
- [Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data](https://mlsanity.com/paper/2405.02067/) (2024)

## Abstract (arXiv)

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user's update. Inspired by the two challenges, we propose FedXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FedXGB mainly achieves the following two breakthroughs. First, FedXGB involves a new HE based secure aggregation scheme for FL. By combining the advantages of secret sharing and homomorphic encryption, the algorithm can solve the second challenge mentioned above, and is robust to the user dropout. Then, FedXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. Moreover, we conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FedXGB. The results indicate that FedXGB achieves less than 1% accuracy loss compared with the original XGBoost, and can provide about 23.9% runtime and 33.3% communication reduction for HE based model update aggregation of FL.