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LF3PFL: A Practical Privacy-Preserving Federated Learning Algorithm Based on Local Federalization Scheme.

Yong Li1,2,3, Gaochao Xu1, Xutao Meng2

  • 1School of Computer Science and Technology, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)
|May 24, 2024
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Summary
This summary is machine-generated.

Localized federated updates (LF3PFL) enhance privacy in federated learning without sacrificing performance. This novel approach improves data confidentiality and model efficacy, offering a practical solution for secure machine learning.

Keywords:
differential privacyfederated learninglocal federalizationprivacy preserving

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Federated learning (FL) raises privacy concerns due to model data exchange.
  • Existing privacy methods like differential privacy (DP) and secure multi-party computing (SMC) have performance and implementation challenges.

Purpose of the Study:

  • To propose and evaluate a novel, pragmatic approach for privacy preservation in FL.
  • To enhance participant data protection and model efficacy using localized federated updates (LF3PFL).

Main Methods:

  • Developed and implemented the localized federated updates (LF3PFL) approach.
  • Incorporated cross-entropy optimization, fine-tuning, and information loss reduction.
  • Validated theoretically and empirically on CIFAR-10, Shakespeare, and MNIST datasets with five local models (Simple-CNN, ModerateCNN, Lenet, VGG9, Resnet18).

Main Results:

  • LF3PFL maintains competitive training accuracies across various models and datasets.
  • Demonstrated significant improvements in privacy preservation compared to state-of-the-art techniques.
  • Achieved a strong balance between model performance and data confidentiality.

Conclusions:

  • LF3PFL offers a scalable and effective solution for privacy preservation in federated learning.
  • Localized federated updates are a promising key component for future FL privacy strategies.
  • The approach addresses practical challenges, enhancing both security and usability in FL applications.