A Survey on Tree Aggregation

Kwatra, Saloni; Torra, Vicenç

doi:10.1109/fuzz45933.2021.9494546

Cited by 3 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The tree selection method usually employs the similarity to select the most similar and representative decision tree with other models [112]. According to previous research [113], we classify the aggregation of decision trees, as shown in Table 3.…”

Section: Data Security Aggregationmentioning

confidence: 99%

“…Each record in the dataset is classified by the tree and assigned to a specific leaf. By focusing solely on the hierarchical structure rather than the queries associated with each node, the aggregation of decision trees can be understood as the aggregation of these hierarchical structures [113]. Kargupta et al [114] proposed a method for tree aggregation based on datasets.…”

Section: Selecting Decision Treesmentioning

confidence: 99%

See 1 more Smart Citation

Decision Tree-Based Federated Learning: A Survey

Wang,

Gai

2024

Blockchains

View full text Add to dashboard Cite

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.

show abstract

Section: Data Security Aggregationmentioning

confidence: 99%

Section: Selecting Decision Treesmentioning

confidence: 99%