RevFRF: Enabling Cross-Domain Random Forest Training With Revocable Federated Learning
Abstract
Random forest is one of the most heated machine learning tools in a wide range of industrial scenarios. Recently, federated learning enables efficient distributed machine learning without direct revealing of private participant data. In this article, we present a novel framework of federated random forest (RevFRF), and further emphatically discuss the participant revocation problem of federated learning based on RevFRF. Specifically, RevFRF first introduces a suite of homomorphic encryption based secure protocols to implement federated random forest (RF). The protocols cover the whole lifecycle of an RF model, including construction, prediction and participant revocation. Then, referring to the practical application scenarios of RevFRF, the existing federated learning frameworks ignore a fact that even every participant in federated learning cannot maintain the cooperation with others forever. In company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from an off-lying company potentially leads to a significant conflict of interest. Therefore, we propose the revocable federated learning concept and illustrate how RevFRF implements participant revocation in applications. Through theoretical analysis and experiments, we show that the protocols can efficiently implement federated RF and ensure the memories of a revoked participant in the trained RF to be securely removed.
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There are already some relevant schemes, such as the backdoor-based verification mechanism in Reference [44] and the encryption-based verification scheme in Reference [111].
“There are already some relevant schemes, such as the backdoorbased verification mechanism in Reference [44] and the encryption-based verification scheme in Reference [111].”
From Machine Unlearning: A Survey (2024) · cites this paper as [111]
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- Authors
- Liu, Y., Ma, Z., Yang, Y., Liu, X., Ma, J., & Ren, K.
- Affiliations
- Xidian University (CN)
- Fuzhou University (CN)
- Zhejiang University (CN)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2022-11-01
- Volume
- 19
- Issue
- 6
- Pages
- 3671-3685
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W3193911850
- Cited by
- 49
- References
- 43
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 14 · 2023: 6 · 2022: 4 · 2021: 1
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61872283, U1764263, 62072109, U1804263)
- CNKLSTISS through the China 111 (B16037)
- Shaanxi Science & Technology Coordination & Innovation Project (2016TZCG-6-3)
- Key R&D Program of Shaanxi Province (2019ZDLGY12-04, 2020ZDLGY09-06)
- Natural Science Basic Research Program of Shaanxi (2021JC-22)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- homomorphic encryption, secure multi-party computation, privacy-preserving machine learning, cross-domain learning, distributed training
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