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A scalable blockchain-enabled federated learning architecture for edge computing.

Shuyang Ren1, Eunsam Kim2, Choonhwa Lee3

  • 1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.

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Summary
This summary is machine-generated.

This study introduces FLCoin, a novel blockchain and federated learning (FL) system for edge computing. FLCoin enhances efficiency and scalability in Internet of Things (IoT) networks by optimizing consensus processing.

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Area of Science:

  • * Distributed Systems and Artificial Intelligence
  • * Blockchain Technology and Edge Computing

Background:

  • * Existing deep learning and blockchain solutions for edge data processing often neglect the significant resource demands of blockchain consensus mechanisms within Internet of Things (IoT) environments.
  • * Federated learning (FL) offers a privacy-preserving approach to distributed machine learning but requires efficient integration with underlying network infrastructures.

Purpose of the Study:

  • * To propose and evaluate FLCoin, a novel system integrating blockchain and federated learning for efficient edge data processing in IoT networks.
  • * To address the limitations of current blockchain-based federated learning approaches by optimizing consensus processing and reducing resource overhead.

Main Methods:

  • * Development of a two-layer blockchain architecture tailored for federated learning (FL) processing.
  • * Introduction of a novel committee-based consensus mechanism where committee members are elected through the FL process.
  • * Experimental validation using the MNIST dataset to train a convolutional neural network (CNN) model.

Main Results:

  • * FLCoin demonstrates stable communication overhead irrespective of network size, ensuring system scalability.
  • * Consensus latency remained below 3 seconds even with increased participating nodes, leading to reduced overall training times.
  • * Achieved a 90% reduction in communication overhead and a 35% decrease in training time cost compared to a similar system using PBFT consensus.

Conclusions:

  • * FLCoin provides an efficient and scalable solution for integrating blockchain and federated learning in IoT edge networks.
  • * The proposed architecture effectively minimizes resource requirements for consensus processing, making it suitable for resource-constrained IoT environments.
  • * FLCoin lays a robust foundation for developing advanced intelligent IoT services through secure and efficient distributed intelligence.