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Related Experiment Video

Updated: Oct 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Asynchronous Federated Learning System Based on Permissioned Blockchains.

Rong Wang1, Wei-Tek Tsai1

  • 1Digital Society & Blockchain Laboratory, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel asynchronous federated learning system using permissioned blockchains to enhance security and privacy. The new framework addresses challenges like differentiated computing power and single points of failure in traditional federated learning.

Keywords:
IoTasynchronous federated learningmulti-blockchains architecturepermissioned blockchainsprivacy protection

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704

Area of Science:

  • Computer Science
  • Cybersecurity
  • Distributed Systems

Background:

  • Existing federated learning (FL) frameworks rely on centralized coordinators, presenting security vulnerabilities.
  • Challenges include device heterogeneity, single points of failure, privacy concerns, and lack of Byzantine fault tolerance.

Purpose of the Study:

  • To propose an asynchronous federated learning system leveraging permissioned blockchains.
  • To enhance security, privacy, and fault tolerance in federated learning environments.

Main Methods:

  • A permissioned blockchain architecture with a main-blockchain and sub-blockchains for model parameter updates.
  • An asynchronous aggregation protocol integrating learned models into the blockchain for two-order aggregation.
  • Simulation experiments to evaluate system performance.

Main Results:

  • The proposed system effectively alleviates issues in synchronous federated learning.
  • Maintains good training performance with malicious nodes and varied data quality.
  • Demonstrates robust fault tolerance.

Conclusions:

  • The asynchronous federated learning system based on permissioned blockchains offers improved security and reliability.
  • Suitable for edge computing scenarios requiring decentralized and secure model training.