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Data mining

Federated Blockchain-Based Incentive and Selection Mechanism Improves Data Sharing and Analysis

Strategic Insights

— 1 min read

In a study published in the journal Pervasive and Mobile Computing, researchers from Hohai University have proposed a federated blockchain-based incentive and selection mechanism for a federated learning system. The new mechanism aims to improve data sharing and analysis by selecting clients using support vector machines (SVM) and providing a real-time incentive for collaborative learning using deep Q reinforcement learning. The proposed system is found to be safe against background knowledge attacks, according to security analysis.

Key Takeaways:

  • The study proposes a federated blockchain-based incentive and selection mechanism for a federated learning system to improve data sharing and analysis.
  • Clients are selected using support vector machines (SVM), while the accuracy of SVM is improved by recursive feature elimination (RFE).
  • A real-time incentive is provided to clients for collaborative learning using deep Q reinforcement learning.
  • An optimal incentive allocation policy is derived using the Markov decision process (MDP) framework.
  • A proof of utility consensus is proposed using a sixteen-round addition game for miners' selection.
  • The proposed SVM-RFE model outperforms existing algorithms in experiments.
  • The proposed system is safe against background knowledge attacks, according to security analysis.
  • The system model is evaluated using extensive simulations and its performance is determined by its optimal statistical utility, system utility, and client utility.

Statistics:

  • 16 rounds of addition game are proposed for miners' selection.
  • The proposed SVM-RFE model outperforms existing algorithms in experiments.
  • 100% of the proposed system is safe against background knowledge attacks, according to security analysis.
  • The system model is evaluated using extensive simulations, with results showing its optimal statistical utility, system utility, and client utility.

Sources:

  • NewsRx. New Blockchain Technology Study Results Reported from Hohai University (A Federated Learning-based Selection and Incentive System Using Blockchain Technology). Journal of Engineering. October 20, 2025; p 1552.
  • A Federated Learning-based Selection and Incentive System Using Blockchain Technology. Pervasive and Mobile Computing, 2025;112.
  • Hohai University. College of Computing and Information. Nanjing 210098, People's Republic of China.

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