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Communication-Efficient Federated Learning for Decision Trees

Publication ,  Journal Article
Zhao, S; Zhu, Z; Li, X; Chen, YC
Published in: IEEE Transactions on Artificial Intelligence
January 1, 2024

The increasing concerns about data privacy and security have driven the emergence of federated learning, which preserves privacy by collaborative learning across multiple clients without sharing their raw data. In this article, we propose a communication-efficient federated learning algorithm for decision trees (DTs), referred to as FL-DT. The key idea is to exchange the statistics of a small number of features among the server and all clients, enabling identification of the optimal feature to split each DT node without compromising privacy. To efficiently find the splitting feature based on the partially available information at each DT node, a novel formulation is derived to estimate the lower and upper bounds of Gini indexes of all features by solving a sequence of mixed-integer convex programming problems. Our experimental results based on various public datasets demonstrate that FL-DT can reduce the communication overhead substantially without surrendering any classification accuracy, compared to other conventional methods.

Duke Scholars

Published In

IEEE Transactions on Artificial Intelligence

DOI

EISSN

2691-4581

Publication Date

January 1, 2024

Volume

5

Issue

11

Start / End Page

5478 / 5492

Related Subject Headings

  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
 

Citation

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ICMJE
MLA
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Zhao, S., Zhu, Z., Li, X., & Chen, Y. C. (2024). Communication-Efficient Federated Learning for Decision Trees. IEEE Transactions on Artificial Intelligence, 5(11), 5478–5492. https://doi.org/10.1109/TAI.2024.3433419
Zhao, S., Z. Zhu, X. Li, and Y. C. Chen. “Communication-Efficient Federated Learning for Decision Trees.” IEEE Transactions on Artificial Intelligence 5, no. 11 (January 1, 2024): 5478–92. https://doi.org/10.1109/TAI.2024.3433419.
Zhao S, Zhu Z, Li X, Chen YC. Communication-Efficient Federated Learning for Decision Trees. IEEE Transactions on Artificial Intelligence. 2024 Jan 1;5(11):5478–92.
Zhao, S., et al. “Communication-Efficient Federated Learning for Decision Trees.” IEEE Transactions on Artificial Intelligence, vol. 5, no. 11, Jan. 2024, pp. 5478–92. Scopus, doi:10.1109/TAI.2024.3433419.
Zhao S, Zhu Z, Li X, Chen YC. Communication-Efficient Federated Learning for Decision Trees. IEEE Transactions on Artificial Intelligence. 2024 Jan 1;5(11):5478–5492.

Published In

IEEE Transactions on Artificial Intelligence

DOI

EISSN

2691-4581

Publication Date

January 1, 2024

Volume

5

Issue

11

Start / End Page

5478 / 5492

Related Subject Headings

  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence