A differential privacy preserving algorithm for greedy decision tree

Yang, Shudan; Sun, Daozhu; Du, Qiming; Liu, Wenfu

doi:10.1109/icbase53849.2021.00050

Cited by 1 publication

(1 citation statement)

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“…However, the addition of noise will directly affect the accuracy of the final model, so the trade-off between noise and error has been investigated. Currently, there are many studies on training decision tree models under centralized differential privacy (CDP) [39,100,101]. Most of them are based on the decision forest, and due to the strict constraints of CDP, there will be precision loss when it is extended to federal settings.…”

Section: Different Privacymentioning

confidence: 99%

Decision Tree-Based Federated Learning: A Survey

Wang,

Gai

2024

Blockchains

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Federated learning (FL) has garnered significant attention as a novel machine learning technique that enables collaborative training among multiple parties without exposing raw local data. In comparison to traditional neural networks or linear models, decision tree models offer higher simplicity and interpretability. The integration of FL technology with decision tree models holds immense potential for performance enhancement and privacy improvement. One current challenge is to identify methods for training and prediction of decision tree models in the FL environment. This survey addresses this issue and examines recent efforts to integrate federated learning and decision tree technologies. We review research outcomes achieved in federated decision trees and emphasize that data security and communication efficiency are crucial focal points for FL. The survey discusses key findings related to data privacy and security issues, as well as communication efficiency problems in federated decision tree models. The primary research outcomes of this paper aim to provide theoretical support for the engineering of federated learning with decision trees as the underlying training model.

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Section: Different Privacymentioning

confidence: 99%