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Updated: Aug 22, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data.

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  • 1Computer Engineering Department, Gachon University, Seongnam 1342, Korea.

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|November 11, 2022
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This study introduces a hierarchical blockchain for privacy-preserving federated learning, preventing model-poisoning attacks without a central curator. Performance analysis of this secure, decentralized system was conducted.

Keywords:
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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Federated learning enables collaborative machine learning by sharing model parameters, not raw data, preserving user privacy.
  • Decentralized aggregation in federated learning poses challenges, including vulnerability to model-poisoning attacks and reliance on trusted curators.
  • Blockchain technology offers a potential solution for secure, decentralized data management and transaction verification.

Purpose of the Study:

  • To design and implement a hierarchical blockchain system for federated learning without a trusted curator.
  • To prevent model-poisoning attacks and ensure secure global model updates in a decentralized environment.
  • To empirically characterize the performance of the proposed federated learning system and identify bottlenecks.

Main Methods:

  • Development of a hierarchical blockchain architecture utilizing a public blockchain.
  • Implementation of a federated learning process integrated with the blockchain system.
  • Comprehensive empirical study and performance analysis within a dedicated testbed.

Main Results:

  • Successful implementation of a federated learning system secured by a hierarchical blockchain.
  • Demonstrated prevention of model-poisoning attacks through decentralized, curated updates.
  • Identification of key performance characteristics and potential bottlenecks within the system.

Conclusions:

  • The hierarchical blockchain system provides a viable, secure, and curator-less approach to federated learning.
  • The system enhances security against adversarial attacks while maintaining collaborative model training.
  • Empirical analysis offers valuable insights for optimizing decentralized federated learning performance.