Blockchain-Based Federated Learning Verification
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Solution Overview
Problem
Current federated learning systems face challenges such as lack of mutual trust among participants, inadequate quality verification mechanisms, and insufficient data privacy and security protections.
Innovation Solution
The proposed solution involves an improved federated learning method based on blockchain technology, which optimizes participants and data processing by utilizing blockchain to enhance security, reliability, and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented without blockchain verification, then system complexity is reduced and ease of operation is improved, but security and reliability of data processing deteriorate
Solution Approach 1:
The patent introduces blockchain as an intermediary verification layer between federated learning participants. The blockchain network verifies participant identities and data quality without requiring direct trust relationships between participants, thus enhancing security while maintaining system operability through automated smart contract-based verification.
Solution Approach 2:
The patent implements preliminary verification of participant credentials and data quality standards through blockchain before federated learning begins. This preliminary action ensures that only qualified participants with valid data can join the learning process, improving reliability while the automated verification reduces operational complexity during the actual learning process.
2Reliability
If blockchain verification is implemented for all participants, then security and reliability are improved, but processing time and system complexity increase
Solution Approach 1:
The patent performs blockchain verification of participant credentials and data quality in advance, before the federated learning process begins. This preliminary verification ensures reliability while allowing the actual learning process to proceed without repeated verification delays, thus reducing processing time loss.
Solution Approach 2:
The patent implements automated smart contracts that self-verify participant credentials and data quality standards without requiring manual intervention. This self-service verification mechanism improves reliability through consistent automated checks while minimizing processing time by eliminating human review steps.
3Manufacturing precision
If data is shared across organizations for federated learning, then data quality and model accuracy are improved, but data privacy and security risks increase
Solution Approach 1:
The patent introduces blockchain as an intermediary that enables secure data sharing between organizations. The blockchain verifies data quality and participant credentials while maintaining cryptographic privacy protection, allowing model accuracy to improve through multi-organization collaboration without exposing raw data, thus reducing privacy risks.
Solution Approach 2:
The patent implements organization-specific data quality standards and privacy protection measures tailored to each participant's requirements. This local quality approach allows each organization to maintain appropriate security controls for their sensitive data while still contributing to the federated learning model, improving accuracy without compromising individual privacy standards.
4Reliability
If participant verification mechanisms are strengthened, then security and data quality are improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent implements automated smart contracts that self-verify participant credentials and data quality standards without requiring manual intervention. This self-service verification mechanism improves data quality through consistent automated checks while simplifying operations by eliminating complex manual verification processes.
Solution Approach 2:
The patent introduces blockchain as an intermediary that automates the verification of participant identities and data quality. This intermediary handles the complexity of verification logic through smart contracts, improving data quality while shielding users from operational difficulties by providing a unified, automated verification interface.
Data Source
AI summary
The present disclosure relates to a blockchain-based federated learning device, method and system. An electronic device for the blockchain-based federated learning is proposed, including a processing circuit configured to acquire first federated learning related information from a federated learning node, cause verifying whether the federated learning node is able to participate in federated learning based on the first federated learning related information through blockchain, and notify the federated learning side of indication information indicating federated learning nodes that are able to participate in federated learning, so that the indicated federated learning nodes that can participate in federated learning can perform data processing based on federated learning.