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Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients.

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Swarm Learning with Fully Homomorphic Encryption (Swarm-FHE) enhances privacy in collaborative AI training by encrypting model parameters. This method protects against malicious participants and gradient leakage, improving secure distributed learning.

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Area of Science:

  • Artificial Intelligence
  • Cybersecurity
  • Distributed Systems

Background:

  • Swarm Learning (SL) enables collaborative model training without a central server, but faces privacy challenges due to data sensitivity and potential gradient leakage from neural networks like GANs.
  • Existing SL frameworks use blockchain for secure aggregation, yet remain vulnerable to compromised participants who can manipulate others' privacy.
  • The risk of original data reconstruction from model parameters necessitates advanced privacy-preserving techniques in distributed learning.

Purpose of the Study:

  • To propose and evaluate Swarm-FHE, a novel method integrating Swarm Learning with Fully Homomorphic Encryption (FHE) to address privacy concerns in collaborative AI training.
  • To enhance the security of distributed model training against malicious participants and gradient leakage.
  • To leverage blockchain for participant authentication and secure parameter sharing in an encrypted environment.

Main Methods:

  • Implemented Swarm-FHE, a framework that encrypts model parameters using Fully Homomorphic Encryption (FHE) before sharing them among participants in a Swarm Learning environment.
  • Utilized blockchain technology for secure registration and authentication of participants within the Swarm Learning network.
  • Evaluated the method's effectiveness through training convolutional neural networks on CIFAR-10 and MNIST datasets, testing various hyperparameter settings.

Main Results:

  • Swarm-FHE successfully encrypts model parameters, safeguarding against gradient leakage and privacy manipulation by malicious participants.
  • The proposed method demonstrated superior performance compared to existing approaches in secure collaborative training scenarios.
  • Experiments on CIFAR-10 and MNIST datasets validated the efficacy and robustness of Swarm-FHE under different configurations.

Conclusions:

  • Swarm-FHE offers a robust solution for privacy-preserving collaborative AI training by combining Swarm Learning, Fully Homomorphic Encryption, and blockchain technology.
  • The integration of FHE effectively mitigates risks associated with data sensitivity and malicious actors in distributed learning environments.
  • This approach significantly advances the security and privacy standards for decentralized machine learning applications.