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An Interpretable Client Decision Tree Aggregation process for Federated
  Learning

An Interpretable Client Decision Tree Aggregation process for Federated Learning

3 April 2024
Alberto Argente-Garrido
Cristina Zuheros
M. V. Luzón
Francisco Herrera
    FedML
ArXivPDFHTML

Papers citing "An Interpretable Client Decision Tree Aggregation process for Federated Learning"

5 / 5 papers shown
Title
Explainability and Continual Learning meet Federated Learning at the Network Edge
Explainability and Continual Learning meet Federated Learning at the Network Edge
Thomas Tsouparopoulos
Iordanis Koutsopoulos
FedML
41
0
0
11 Apr 2025
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Ming Li
Pengcheng Xu
Junjie Hu
Zeyu Tang
Guang Yang
FedML
50
1
0
15 Sep 2024
Connecting the Dots in Trustworthy Artificial Intelligence: From AI
  Principles, Ethics, and Key Requirements to Responsible AI Systems and
  Regulation
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Natalia Díaz Rodríguez
Javier Del Ser
Mark Coeckelbergh
Marcos López de Prado
E. Herrera-Viedma
Francisco Herrera
XAI
27
268
0
02 May 2023
OpBoost: A Vertical Federated Tree Boosting Framework Based on
  Order-Preserving Desensitization
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Xiaochen Li
Yuke Hu
Weiran Liu
Hanwen Feng
Li Peng
Yuan Hong
Kui Ren
Zhan Qin
FedML
132
26
0
04 Oct 2022
Trustworthy AI: From Principles to Practices
Trustworthy AI: From Principles to Practices
Bo-wen Li
Peng Qi
Bo Liu
Shuai Di
Jingen Liu
Jiquan Pei
Jinfeng Yi
Bowen Zhou
119
356
0
04 Oct 2021
1