An Interpretable Client Decision Tree Aggregation process for Federated Learning

• URL: http://arxiv.org/abs/2404.02510v1
• Date: Wed, 3 Apr 2024 06:53:56 GMT
• Title: An Interpretable Client Decision Tree Aggregation process for Federated Learning
• Authors: Alberto Argente-Garrido, Cristina Zuheros, M. Victoria Luzón, Francisco Herrera,
• Abstract summary: We propose an Interpretable Client Decision Tree aggregation process for Federated Learning scenarios. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art.
• Score: 7.8973037023478785
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory models, are ideal for collaborative model training across multiple devices in resource-constrained environments such as federated learning environments for injecting interpretability in these models. Decision tree structure makes the aggregation in a federated learning environment not trivial. They require techniques that can merge their decision paths without introducing bias or overfitting while keeping the aggregated decision trees robust and generalizable. In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art.

Related papers

• Zero-Shot Decision Tree Construction via Large Language Models [2.005837558796176]
  We introduce an algorithm for constructing decision trees using large language models (LLMs) in a zero-shot manner based on Classification and Regression Trees (CART) principles. Our approach leverages LLMs to perform operations essential for decision tree construction, including attribute discretization, probability calculation, and Gini index computation.
  arXiv  Detail & Related papers  (2025-01-27T17:48:48Z)
• Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Study Case on BERT-based Language Models [7.94649144127614]
  Decision trees are a popular machine learning method, known for their inherent explainability. In Explainable AI, decision trees can be used as surrogate models for complex black box AI models or as approximations of parts of such models. We investigate the use of the Probably Approximately Correct (PAC) framework to provide a theoretical guarantee of fidelity for decision trees extracted from AI models.
  arXiv  Detail & Related papers  (2024-12-13T19:14:08Z)
• Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning [53.241569810013836]
  We propose a novel framework that utilizes large language models (LLMs) to identify effective feature generation rules. We use decision trees to convey this reasoning information, as they can be easily represented in natural language. OCTree consistently enhances the performance of various prediction models across diverse benchmarks.
  arXiv  Detail & Related papers  (2024-06-12T08:31:34Z)
• Learning accurate and interpretable decision trees [27.203303726977616]
  We develop approaches to design decision tree learning algorithms given repeated access to data from the same domain. We study the sample complexity of tuning prior parameters in Bayesian decision tree learning, and extend our results to decision tree regression. We also study the interpretability of the learned decision trees and introduce a data-driven approach for optimizing the explainability versus accuracy trade-off using decision trees.
  arXiv  Detail & Related papers  (2024-05-24T20:10:10Z)
• Learning a Decision Tree Algorithm with Transformers [75.96920867382859]
  We introduce MetaTree, a transformer-based model trained via meta-learning to directly produce strong decision trees. We fit both greedy decision trees and globally optimized decision trees on a large number of datasets, and train MetaTree to produce only the trees that achieve strong generalization performance.
  arXiv  Detail & Related papers  (2024-02-06T07:40:53Z)
• Modeling Boundedly Rational Agents with Latent Inference Budgets [56.24971011281947]
  We introduce a latent inference budget model (L-IBM) that models agents' computational constraints explicitly. L-IBMs make it possible to learn agent models using data from diverse populations of suboptimal actors. We show that L-IBMs match or outperform Boltzmann models of decision-making under uncertainty.
  arXiv  Detail & Related papers  (2023-12-07T03:55:51Z)
• Optimal Decision Diagrams for Classification [68.72078059880018]
  We study the training of optimal decision diagrams from a mathematical programming perspective. We introduce a novel mixed-integer linear programming model for training. We show how this model can be easily extended for fairness, parsimony, and stability notions.
  arXiv  Detail & Related papers  (2022-05-28T18:31:23Z)
• Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning [11.843365055516566]
  Fed-EINI is an efficient and interpretable inference framework for federated decision tree models. We propose to protect the decision path by the efficient additively homomorphic encryption method. Experiments show that the inference efficiency is improved by over $50%$ in average.
  arXiv  Detail & Related papers  (2021-05-20T06:40:05Z)
• Fair Training of Decision Tree Classifiers [6.381149074212897]
  We study the problem of formally verifying individual fairness of decision tree ensembles. In our approach, fairness verification and fairness-aware training both rely on a notion of stability of a classification model.
  arXiv  Detail & Related papers  (2021-01-04T12:04:22Z)
• Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity [79.83903179393164]
  This paper further extends the deep forest idea in several important aspects. We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
  arXiv  Detail & Related papers  (2020-12-29T18:05:05Z)
• Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning [66.01622034708319]
  We propose a knowledge distillation based decision trees extension, dubbed rectified decision trees (ReDT) We extend the splitting criteria and the ending condition of the standard decision trees, which allows training with soft labels. We then train the ReDT based on the soft label distilled from a well-trained teacher model through a novel jackknife-based method.
  arXiv  Detail & Related papers  (2020-08-21T10:45:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.