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Blockchain for deep learning: review and open challenges.

Muhammad Shafay1, Raja Wasim Ahmad1,2, Khaled Salah1

  • 1Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788 UAE.

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Summary
This summary is machine-generated.

Integrating blockchain technology with deep learning enhances transparency and security. This study reviews and categorizes existing literature on blockchain-based deep learning frameworks, identifying key challenges for future development.

Keywords:
AIBlockchainDeep learningEthereumFederated learningMachine learningSecuritySmart contractsTransparency

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Information Security

Background:

  • Centralized deep learning systems lack transparency, traceability, and security.
  • Centralized data training poses a single point of failure risk.
  • Blockchain technology offers potential solutions to these limitations.

Purpose of the Study:

  • To explore the integration of blockchain technology with deep learning.
  • To review and categorize existing literature on this integration.
  • To identify research challenges for trustworthy blockchain-based deep learning.

Main Methods:

  • Literature review of blockchain and deep learning integration.
  • Thematic taxonomy based on seven parameters (blockchain type, DL models, consensus protocols, application area, services, data types, deployment goals).
  • Comparative analysis of frameworks based on four parameters (blockchain type, consensus protocol, DL method, dataset).

Main Results:

  • Classification and categorization of existing literature.
  • Discussion of strengths and weaknesses of current blockchain-based deep learning frameworks.
  • Identification of key research challenges.

Conclusions:

  • Blockchain integration can enhance trust, transparency, and security in deep learning.
  • Further research is needed to address identified challenges for robust frameworks.
  • The study provides a comprehensive overview and taxonomy for the field.