An Interpretable Client Decision Tree Aggregation Process for Federated Learning to Improve Model Performance and Maintain Interpretability

핵심 개념

The proposed Interpretable Client Decision Tree Aggregator For Federated Learning (ICDTA4FL) process aggregates multiple client decision trees into a global interpretable decision tree model, improving performance over local models while maintaining the inherent interpretability of decision trees.

초록

The ICDTA4FL process works as follows:

1. Clients train local decision tree models using their private data and send them to the server.
2. The server evaluates the local decision trees sent by the clients on each client's data and receives the evaluation metrics.
3. The server filters out low-performing local decision trees based on the evaluation metrics.
4. The server extracts the decision rules from the remaining local decision trees and aggregates them using the Cartesian product, ensuring compatibility between rules.
5. The server builds a global decision tree using the aggregated rules.
6. The server sends the global decision tree back to the clients.
7. Clients evaluate the global decision tree on their local data.

The ICDTA4FL process is designed to work with different decision tree algorithms, and the paper presents two specific models: ICDTA4FL-ID3 and ICDTA4FL-CART. The experiments show that the ICDTA4FL process improves the performance of the local decision tree models while maintaining the interpretability of the global model. The ICDTA4FL-ID3 model outperforms the state-of-the-art Federated-ID3 model, and the ICDTA4FL-CART model performs well compared to the ICDTA4FL-ID3 model, especially in numerical datasets with fewer clients.

통계

The number of instances in the datasets ranges from 1,728 to 48,842.
The number of features ranges from 6 to 24.
The number of classes ranges from 2 to 5.

인용구

"Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others."
"Decision Trees (DTs) are an example of self-explanatory models because their structure is inherently interpretable, facilitating transparency and trust among stakeholders in FL environments."
"Aggregating DTs in an FL environment presents particular challenges due to the structure and characteristics of DTs, along with the intrinsic nature of FL."

핵심 통찰 요약

An Interpretable Client Decision Tree Aggregation process for Federated Learning

by Albe... 게시일 arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02510.pdf

더 깊은 질문

How can the ICDTA4FL process be extended to handle more complex decision tree models, such as ensemble methods like Random Forests and Gradient Boosting Decision Trees

To extend the ICDTA4FL process to handle more complex decision tree models like Random Forests and Gradient Boosting Decision Trees, we can modify the aggregation step to accommodate ensemble methods. Instead of aggregating individual decision paths, we would aggregate the predictions of multiple trees in the ensemble. For Random Forests, we can aggregate the predictions of each tree and use a voting mechanism to make the final decision. Similarly, for Gradient Boosting Decision Trees, we can aggregate the predictions by summing the outputs of each tree and applying a threshold to make the final prediction. By adapting the aggregation process to consider the ensemble nature of these models, we can leverage the diversity and robustness they offer while maintaining interpretability.

What are the potential trade-offs between the interpretability and the performance of the global model obtained through the ICDTA4FL process, and how can they be balanced

The potential trade-offs between interpretability and performance in the global model obtained through the ICDTA4FL process revolve around the complexity of the model and the level of detail in the decision-making process. A more interpretable model, such as a single decision tree, may sacrifice some performance compared to more complex models like ensemble methods. However, this trade-off can be balanced by optimizing the aggregation process to retain the interpretability of the base models while improving the overall performance of the global model. Techniques like feature importance analysis can help maintain interpretability by highlighting the most influential features in the decision-making process. Additionally, fine-tuning the aggregation process to prioritize important decision paths can enhance performance without compromising interpretability.

How can the ICDTA4FL process be adapted to work in a vertical federated learning setting, where clients have different feature sets but share the same data samples

Adapting the ICDTA4FL process to work in a vertical federated learning setting, where clients have different feature sets but share the same data samples, requires adjustments in the aggregation and rule compatibility steps. In this scenario, the process needs to handle feature mismatches between clients by aligning the features or transforming them into a common format. This can involve feature mapping, where features from different clients are mapped to a common feature space, or feature selection, where only overlapping features are considered for aggregation. Additionally, the rule compatibility check should account for feature variations and ensure that rules are compatible across different feature sets. By addressing these challenges, the ICDTA4FL process can effectively operate in a vertical federated learning setting while maintaining interpretability and performance.