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Training of Classification Models via Federated Learning and Homomorphic Encryption.

Eduardo Angulo1, José Márquez1, Ricardo Villanueva-Polanco1

  • 1Department of Computer Science and Engineering, Universidad del Norte, Barranquilla 081007, Colombia.

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Summary

This study introduces a privacy-preserving protocol for training Multi-Layer Perceptron (MLP) neural networks using federated learning and homomorphic encryption. The method ensures sensitive user data remains secure across multiple clients during model training.

Keywords:
classification modelsfederated learninghomomorphic encryptiontraining

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Area of Science:

  • Machine Learning
  • Data Privacy
  • Cybersecurity

Background:

  • Increasing use of social networks and data protection laws necessitate secure methods for training machine learning models.
  • User data on devices can contain sensitive information, posing risks if leaked.
  • Current methods for decentralized data training require enhanced privacy measures.

Purpose of the Study:

  • To propose a novel protocol for training Multi-Layer Perceptron (MLP) neural networks.
  • To combine federated learning with homomorphic encryption for robust data privacy.
  • To ensure data remains secure and distributed across multiple clients during the training process.

Main Methods:

  • Developed a protocol integrating federated learning and homomorphic encryption for MLP training.
  • Conducted simulations using a multi-class classification dataset.
  • Varied MLP architectures and the number of participating clients to test the protocol's efficacy.

Main Results:

  • Validated the proposed protocol through extensive simulations.
  • Presented performance metrics in both local and federated settings.
  • Conducted a comparative analysis against existing methods.

Conclusions:

  • The proposed protocol effectively preserves data privacy during federated MLP training.
  • Formal analysis confirms the privacy guarantees under defined assumptions.
  • The protocol offers significant added value compared to previous approaches in secure machine learning.