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Related Experiment Videos

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving

Yu Ding1, Carlos Costa2, Bing Si1

  • 1Thomas J. Watson College of Engineering and Applied Science at Binghamton University, Binghamton, NY 13902 USA.

IEEE Transactions on Automation Science and Engineering : a Publication of the IEEE Robotics and Automation Society
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel federated Gradient Boosting algorithm (fed-GB-LSA) for privacy-preserving functional regression. It achieves efficient, high-performance federated learning without sharing sensitive data.

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Data Privacy

Background:

  • Federated Learning (FL) enables collaborative model training across decentralized data sources while preserving privacy.
  • A key challenge in FL is ensuring the federated model's performance matches a centrally trained global model.
  • Research on FL for functional regression models, which analyze functional data, is limited.

Purpose of the Study:

  • To develop an efficient and privacy-preserving federated learning algorithm for function-on-function regression.
  • To address the challenge of achieving comparable performance in federated models without data sharing.

Main Methods:

  • Development of the federated Gradient Boosting with Least Squares Approximation (fed-GB-LSA) algorithm.
  • Leveraging Gradient Boosting for sparse feature selection in functional regression.
Keywords:
Federated learningfunctional regressiongradient boostingtelemedicine

Related Experiment Videos

  • Utilizing Least Squares Approximation for efficient, one-shot federated learning.

    • Main Results:

    • The fed-GB-LSA algorithm enables efficient, privacy-preserving federated learning for function-on-function regression.
    • The method allows for sparse selection of functional and non-functional features.
    • Theoretical guarantees for federated model performance are provided without cross-server data sharing.

    Conclusions:

    • The proposed fed-GB-LSA is the first algorithm for federated learning of function-on-function regression.
    • It offers communication and statistical efficiency, validated through simulations and a real-world application in Obstructive Sleep Apnea telemonitoring.