Основные понятия
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.
Аннотация
The ICDTA4FL process works as follows:
- Clients train local decision tree models using their private data and send them to the server.
- The server evaluates the local decision trees sent by the clients on each client's data and receives the evaluation metrics.
- The server filters out low-performing local decision trees based on the evaluation metrics.
- The server extracts the decision rules from the remaining local decision trees and aggregates them using the Cartesian product, ensuring compatibility between rules.
- The server builds a global decision tree using the aggregated rules.
- The server sends the global decision tree back to the clients.
- 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.
Статистика
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.
Цитаты
"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."