Cloud-based Federated Boosting for Mobile Crowdsensing

By Zhuzhu Wang, Yilong Yang, Yang Liu, Ximeng Liu, Brij Bhooshan Gupta, Jianfeng Ma. 2020

14 citations recorded by the source corpus.

The application of federated extreme gradient boosting to mobile crowdsensing apps brings several benefits, in particular high performance on efficiency and classification. However, it also brings a new challenge for data and model privacy protection. Besides it being vulnerable to Generative Adversarial Network (GAN) based user data reconstruction attack, there is not the existing architecture that considers how to preserve model privacy. In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing. Specifically, we first build a secure classification and regression tree (CART) of XGBoost using secret sharing. Then, we propose a secure prediction protocol to protect the model privacy of XGBoost in mobile crowdsensing. We conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FedXGB. The results indicate that FedXGB is secure against the honest-but-curious adversaries and attains less than 1% accuracy loss compared with the original XGBoost model.

Subfields: Artificial Intelligence, Computer Science Applications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Murali Annavaram
  2. Qiang Yang
  3. Chaoyang He
  4. Yang Liu
  5. S. Avestimehr
  6. Praneeth Vepakomma
  7. Abhishek Singh
  8. Ramesh Raskar

In Computer Science Applications

  1. Peilin Zhao
  2. Murali Annavaram
  3. Ramesh Raskar
  4. Qiang Yang
  5. Praneeth Vepakomma
  6. Chaoyang He
  7. Yang Liu

Citations in the corpus