Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2020

Abstract

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.

Keywords

Privacy-Preserving, Federated learning, Extreme gradient boosting, Mobile crowdsensing

Discipline

Information Security

Research Areas

Cybersecurity

Publication

2020 40th IEEE International Conference on Distributed Computing Systems (ICDCS): Singapore, November 29 - December 1: Proceedings

First Page

1

Last Page

11

ISBN

9781728170022

Identifier

10.1109/ICDCS47774.2020.00017

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Embargo Period

5-10-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICDCS47774.2020.00017

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