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This study introduces a secure aggregation protocol for decentralized federated learning (FL) using Multi-Secret-Sharing and Dining Cryptographers Network. The new protocol enhances data privacy without a central server, achieving comparable results to traditional FL methods.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Cryptography

Background:

  • Big data generation necessitates machine learning model training.
  • Sensitive data in training poses privacy risks and regulatory challenges.
  • Federated Learning (FL) offers a privacy-preserving approach but remains vulnerable to data reconstruction attacks.

Purpose of the Study:

  • To propose a secure aggregation protocol for Decentralized Federated Learning (DFL).
  • To enhance data privacy in FL by eliminating the need for a central server.
  • To provide a privacy-preserving alternative to existing FL aggregation methods.

Main Methods:

  • Developed a secure aggregation protocol combining Multi-Secret-Sharing (MSS) with a Dining Cryptographers Network (DCN).
  • Implemented and validated the protocol in simulations using the MNIST handwritten digits dataset.
  • Compared the protocol's performance against standard Federated Learning with the FedAvg protocol.

Main Results:

  • The proposed DFL protocol achieves comparable accuracy to FedAvg.
  • The protocol significantly enhances user data privacy against potential attacks.
  • Timing performance is efficient, avoiding the significant overhead associated with Homomorphic Encryption.

Conclusions:

  • The novel DFL protocol effectively secures sensitive data during machine learning model training.
  • The combination of MSS and DCN provides a robust and efficient privacy-preserving solution.
  • This approach advances secure decentralized machine learning practices.